ISLAB/CAISR - User contributions [en]

Anomaly Detection for Predictive Maintenance with Elvaco

2020-10-26T15:44:31Z

YuantaoFan: Created page with "{{StudentProjectTemplate |Summary=Anomaly detection in sensor data for predictive maintenance purpose, in collaboration with Elvaco |TimeFrame=Fall 2020 |Supervisor=Mohamed-Ra..."

{{StudentProjectTemplate
|Summary=Anomaly detection in sensor data for predictive maintenance purpose, in collaboration with Elvaco
|TimeFrame=Fall 2020
|Supervisor=Mohamed-Rafik Bouguelia, Yuantao Fan,
|Level=Master
|Status=Draft
}}
Detect anomalies in sensor data for finding faults, e.g. sensor errors, water leakages in heating systems, in collaboration with Elvaco.

Transfer Learning for Machine Diagnosis and Prognosis

2020-10-06T18:32:30Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Study and develop deep adversarial neural networks (DANN) based methods to detect faults and predict failures in industrial equipment, under transfer learning scenarios.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault Diagnosis, Prognosis
|TimeFrame=Fall 2020
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Zhuang, Fuzhen, et al. "A comprehensive survey on transfer learning." Proceedings of the IEEE (2020).

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model-based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Guo, Liang, et al. "Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data." IEEE Transactions on Industrial Electronics 66.9 (2018): 7316-7325.

[6] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain adaptive transfer learning for fault diagnosis." 2019 Prognostics and System Health Management Conference (PHM-Paris). IEEE, 2019.

[7] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[8] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[9] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural
Information Processing Systems. 2016.

[10] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for remaining useful life prediction based on consensus self-organizing models." Reliability Engineering & System Safety 203 (2020): 107098.

[11] Zheng, Huailiang, et al. "Cross-domain fault diagnosis using knowledge transfer strategy: A review." IEEE Access 7 (2019): 129260-129290.
|Prerequisites=Artificial Intelligence, Data Mining, and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Peyman Mashhadi, Yuantao Fan, Mohammed Ghaith Altarabichi
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2, 11] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in many fields, especially in the application area of machine diagnosis and prognosis. The current industrial approach for machine diagnosis and prognosis usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment, which might not represent the (operating/environment) conditions after being deployed to the real-world application. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on usages patterns, faults, and equipment deterioration patterns are very challenging. Utilizing available data (including fault and failure cases) from other equipment that shares a similar mechanical structure and/or being operated in a similar way with adaptation will be helpful if done properly.

Therefore, machine diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to apply transfer learning (or domain adaptation) methods, utilize and transfer knowledge between different tasks, e.g. [6], and/or dealing with new conditions/faults, e.g. [10]. As a popular feature-representation-based TL method, Domain Adversarial Neural Networks (DANN) [3, 9] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 7, 8]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression tasks.

The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, etc., to perform machine diagnosis and prognosis, under transfer learning scenarios. The thesis is expected to address the generality of DANN based approaches in dealing with different types of transfer learning scenarios. The proposed method will be evaluated using simulated data and/or real data from industrial systems.

Music style transfer

2020-10-06T18:29:34Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a system that receives a piece of music in one genre and changes/transfers its style into another genre, using machine learning algorithms.
|Keywords=Deep Learning, Neural Networks, music style, genre, domain, transfer
|TimeFrame=Fall 2020
|References=[1] Dai, Shuqi, Zheng Zhang, and Gus G. Xia. "Music style transfer: A position paper." arXiv preprint arXiv:1803.06841(2018).

[2] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "Image style transfer using convolutional neural networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

[3] Fazıl Say, Alla Turca Jazz. https://youtu.be/WWftABQV4Wk

[4] Taro Hakase et al., BWV 1043 Jazz. https://youtu.be/2OiBX07ImA0

[5] Hung, Yun-Ning, et al. "Musical composition style transfer via disentangled timbre representations." arXiv preprint arXiv:1905.13567 (2019).

[6] Brunner, Gino, et al. "Symbolic music genre transfer with CycleGAN." 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2018.

[7] Brunner, Gino, et al. "MIDI-VAE: Modeling dynamics and instrumentation of music with applications to style transfer." arXiv preprint arXiv:1809.07600 (2018).
|Prerequisites=Artificial Intelligence, Data Mining, and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms; interests in music (of many genres)
|Supervisor=Peyman Mashhadi, Yuantao Fan
|Level=Master
|Status=Open
}}
Music style transfer [1, 5, 6, 7] can be considered as the counterpart of image style transfer [2]. The aim of this thesis project is to develop a system that, given a piece of music in one genre, changes its style into another genre. For example, this transition can be from classical to jazz, e.g. Alla Turca Jazz by Fazıl Say [3], and Bach Jazz such as BWV.1043 by Taro Hakase [4]. The specific type of music style transfer, in this work, is Composition Style Transfer [1, 5], i.e. preserving the identifiable melody contour of the input pieces, while altering some other score features in a meaningful way, i.e. interpretation in other music style/genre. One of the challenges when it comes to study/research music from a scientific perspective is that music is, by nature, very subjective and it is difficult to evaluate the results objectively. In this work, the genre of the music pieces will be evaluated using a trained genre classifier, which discriminates different genres from each other.

One approach to address music style transfer is using adversarial deep networks [6]. A generator takes a piece of music in a specific genre as input and tries to generate the transferred version of the same piece in another genre. A discriminator then tries to discern between generated music and real music. This way through adversarial training, the generator will hopefully end up generating a genre-transferred version of the inputs. The generated genre-transferred music can be evaluated using a genre classifier. The mentioned architecture is one way of doing a style transfer.

Music style transfer

2020-10-05T14:20:44Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a system that receives a piece of music in one genre and changes/transfers its style into another genre, using machine learning algorithms.
|Keywords=Deep Learning, Neural Networks, music style, genre, domain, transfer
|TimeFrame=Fall 2020
|References=[1] Dai, Shuqi, Zheng Zhang, and Gus G. Xia. "Music style transfer: A position paper." arXiv preprint arXiv:1803.06841(2018).

[2] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "Image style transfer using convolutional neural networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

[3] Fazıl Say, Alla Turca Jazz. https://youtu.be/WWftABQV4Wk

[4] Taro Hakase et al., BWV 1043 Jazz. https://youtu.be/2OiBX07ImA0

[5] Hung, Yun-Ning, et al. "Musical composition style transfer via disentangled timbre representations." arXiv preprint arXiv:1905.13567 (2019).

[6] Brunner, Gino, et al. "Symbolic music genre transfer with CycleGAN." 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2018.

[7] Brunner, Gino, et al. "MIDI-VAE: Modeling dynamics and instrumentation of music with applications to style transfer." arXiv preprint arXiv:1809.07600 (2018).
|Prerequisites=Artificial Intelligence, Data Mining, and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms; interests in music (of many genres)
|Supervisor=Peyman Mashhadi, Yuantao Fan
|Level=Master
|Status=Open
}}
Music style transfer [1, 5, 6, 7] can be considered as the counterpart of image style transfer [2]. The aim of this thesis project is to develop a system that, given a piece of music in one genre, changes its style into another genre. For example, this transition can be from classical to jazz, e.g. Alla Turca Jazz by Fazıl Say [3], and Bach Jazz such as BWV.1043 by Taro Hakase [4]. The specific type of music style transfer, in this work, is Composition Style Transfer [1, 5], i.e. preserving the identifiable melody contour of the input pieces, while altering some other score features in a meaningful way, i.e. interpretation in other music style/genre. One of the challenges when it comes to study/research music from a scientific perspective is that music is, by nature, very subjective and it is difficult to evaluate the results objectively. In this work, the genre of the music pieces will be evaluated, objectively, using a trained genre classifier, which discriminates different genres from each other.

One approach to address music style transfer is using adversarial deep networks [6]. A generator takes a piece of music in a specific genre as input and tries to generate the transferred version of the same piece in another genre. A discriminator then tries to discern between generated music and real music. This way through adversarial training, the generator will hopefully end up generating a genre-transferred version of the inputs. The generated genre-transferred music can be evaluated using a genre classifier. The mentioned architecture is one way of doing a style transfer.

Music style transfer

2020-10-05T14:17:51Z

YuantaoFan: Created page with "{{StudentProjectTemplate |Summary=Develop a system that receives a piece of music in one genre and changes/transfers its style into another genre, using machine learning algor..."

{{StudentProjectTemplate
|Summary=Develop a system that receives a piece of music in one genre and changes/transfers its style into another genre, using machine learning algorithms.
|Keywords=Deep Learning, Neural Networks, music style, genre, domain, transfer
|TimeFrame=Fall 2020
|References=[1] Dai, Shuqi, Zheng Zhang, and Gus G. Xia. "Music style transfer: A position paper." arXiv preprint arXiv:1803.06841(2018).

[2] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "Image style transfer using convolutional neural networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.

[3] Fazıl Say, Alla Turca Jazz. https://youtu.be/WWftABQV4Wk

[4] Taro Hakase et al. https://youtu.be/2OiBX07ImA0

[5] Hung, Yun-Ning, et al. "Musical composition style transfer via disentangled timbre representations." arXiv preprint arXiv:1905.13567 (2019).

[6] Brunner, Gino, et al. "Symbolic music genre transfer with CycleGAN." 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2018.

[7] Brunner, Gino, et al. "MIDI-VAE: Modeling dynamics and instrumentation of music with applications to style transfer." arXiv preprint arXiv:1809.07600 (2018).
|Prerequisites=Artificial Intelligence, Data Mining, and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms; interests in music (of many genres)
|Supervisor=Peyman Mashhadi, Yuantao Fan
|Level=Master
|Status=Open
}}
Music style transfer [1, 5, 6, 7] can be considered as the counterpart of image style transfer [2]. The aim of this thesis project is to develop a system that, given a piece of music in one genre, changes its style into another genre. For example, this transition can be from classical to jazz, e.g. Alla Turca Jazz by Fazıl Say [3], and Bach Jazz such as BWV.1043 by Taro Hakase [4]. The specific type of music style transfer, in this work, is Composition Style Transfer [1, 5], i.e. preserving the identifiable melody contour of the input pieces, while altering some other score features in a meaningful way, i.e. interpretation in other music style/genre. One of the challenges when it comes to study/research music from a scientific perspective is that music is, by nature, very subjective and it is difficult to evaluate the results objectively. In this work, the genre of the music pieces will be evaluated, objectively, using a trained genre classifier, which discriminates different genres from each other.

One approach to address music style transfer is using adversarial deep networks [6]. A generator takes a piece of music in a specific genre as input and tries to generate the transferred version of the same piece in another genre. A discriminator then tries to discern between generated music and real music. This way through adversarial training, the generator will hopefully end up generating a genre-transferred version of the inputs. The generated genre-transferred music can be evaluated using a genre classifier. The mentioned architecture is one way of doing a style transfer.

Transfer Learning for Machine Diagnosis and Prognosis

2020-10-04T17:11:32Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Study and develop deep adversarial neural networks (DANN) based methods to detect faults and predict failures in industrial equipment, under transfer learning scenarios.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault Diagnosis, Prognosis
|TimeFrame=Fall 2020
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Zhuang, Fuzhen, et al. "A comprehensive survey on transfer learning." Proceedings of the IEEE (2020).

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model-based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Guo, Liang, et al. "Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data." IEEE Transactions on Industrial Electronics 66.9 (2018): 7316-7325.

[6] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain adaptive transfer learning for fault diagnosis." 2019 Prognostics and System Health Management Conference (PHM-Paris). IEEE, 2019.

[7] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[8] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[9] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural
Information Processing Systems. 2016.

[10] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for remaining useful life prediction based on consensus self-organizing models." Reliability Engineering & System Safety 203 (2020): 107098.

[11] Zheng, Huailiang, et al. "Cross-domain fault diagnosis using knowledge transfer strategy: A review." IEEE Access 7 (2019): 129260-129290.
|Prerequisites=Artificial Intelligence, Data Mining, and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Peyman Mashhadi, Yuantao Fan
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2, 11] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in many fields, especially in the application area of machine diagnosis and prognosis. The current industrial approach for machine diagnosis and prognosis usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment, which might not represent the (operating/environment) conditions after being deployed to the real-world application. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on usages patterns, faults, and equipment deterioration patterns are very challenging. Utilizing available data (including fault and failure cases) from other equipment that shares a similar mechanical structure and/or being operated in a similar way with adaptation will be helpful if done properly.

Therefore, machine diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to apply transfer learning (or domain adaptation) methods, utilize and transfer knowledge between different tasks, e.g. [6], and/or dealing with new conditions/faults, e.g. [10]. As a popular feature-representation-based TL method, Domain Adversarial Neural Networks (DANN) [3, 9] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 7, 8]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression tasks.

The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, etc., to perform machine diagnosis and prognosis, under transfer learning scenarios. The thesis is expected to address the generality of DANN based approaches in dealing with different types of transfer learning scenarios. The proposed method will be evaluated using simulated data and/or real data from industrial systems.

Transfer Learning for Machine Diagnosis and Prognosis

2020-10-04T17:00:11Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Study and develop deep adversarial neural networks (DANN) based methods to detect faults and predict failures in industrial equipment, under transfer learning scenarios.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault Diagnosis, Prognosis
|TimeFrame=Fall 2020
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Zhuang, Fuzhen, et al. "A comprehensive survey on transfer learning." Proceedings of the IEEE (2020).

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model-based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Guo, Liang, et al. "Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data." IEEE Transactions on Industrial Electronics 66.9 (2018): 7316-7325.

[6] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain adaptive transfer learning for fault diagnosis." 2019 Prognostics and System Health Management Conference (PHM-Paris). IEEE, 2019.

[7] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[8] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[9] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural
Information Processing Systems. 2016.

[10] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for remaining useful life prediction based on consensus self-organizing models." Reliability Engineering & System Safety 203 (2020): 107098.

[11] Zheng, Huailiang, et al. "Cross-domain fault diagnosis using knowledge transfer strategy: A review." IEEE Access 7 (2019): 129260-129290.
|Prerequisites=Artificial Intelligence, Data Mining, and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Peyman Mashhadi, Yuantao Fan
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2, 11] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in many fields, especially in the application area of machine diagnosis and prognosis. The current industrial approach for developing machine diagnosis and prognosis methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting faults, predicting failures, and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under changing/evolving environmental conditions and were operated in a variety of ways. This reply on the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns, fault types and deterioration patterns are very challenging. Utilizing available data (including fault and failure cases) from other equipment that shares a similar mechanical structure and/or being operated in a similar way with adaptation will be helpful if done properly.

Therefore, machinery diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform feature-representation-based TL (or domain adaptation) methods, for transferring knowledge between tasks, e.g. [6], and/or dealing with new conditions/faults, e.g. [10]. Feature-representation-based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time. Domain Adversarial Neural Networks (DANN) [3, 9] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 7, 8]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression tasks.

The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, etc., to perform machine diagnosis and prognosis, under transfer learning scenarios. The thesis are expected to address the generality of DANN based approaches in dealing with different types of transfer learning scenarios. The proposed method will be evaluated using simulated data and/or real data from industrial systems.

Transfer Learning for Machine Diagnosis and Prognosis

2020-10-04T16:25:43Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Study and develop deep adversarial neural networks (DANN) based methods to detect faults and predict failures in industrial equipment, under transfer learning scenarios.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault Diagnosis, Prognosis
|TimeFrame=Fall 2020
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Zhuang, Fuzhen, et al. "A comprehensive survey on transfer learning." Proceedings of the IEEE (2020).

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model-based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Guo, Liang, et al. "Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data." IEEE Transactions on Industrial Electronics 66.9 (2018): 7316-7325.

[6] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain adaptive transfer learning for fault diagnosis." 2019 Prognostics and System Health Management Conference (PHM-Paris). IEEE, 2019.

[7] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[8] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[9] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural
Information Processing Systems. 2016.

[10] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for remaining useful life prediction based on consensus self-organizing models." Reliability Engineering & System Safety 203 (2020): 107098.

[11] Zheng, Huailiang, et al. "Cross-domain fault diagnosis using knowledge transfer strategy: A review." IEEE Access 7 (2019): 129260-129290.
|Prerequisites=Artificial Intelligence, Data Mining, and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Peyman Mashhadi, Yuantao Fan
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2, 11] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in many fields, especially in the application area of machine diagnosis and prognosis. The current industrial approach for developing machine diagnosis and prognosis methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting faults, predicting failures, and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under changing/evolving environmental conditions and were operated in a variety of ways. This reply on the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns, fault types and deterioration patterns are very challenging. Utilizing available data (including fault and failure cases) from other equipment that shares a similar mechanical structure and/or being operated in a similar way with adaptation will be helpful if done properly.

Therefore, machinery diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform feature-representation-based TL (or domain adaptation) methods, for transferring knowledge between tasks, e.g. [6], and/or dealing with new conditions/faults, e.g. [10]. Feature-representation-based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time. Domain Adversarial Neural Networks (DANN) [3, 9] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 7, 8]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression tasks.

The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, etc., to perform machine diagnosis and prognosis, under transfer learning scenarios. The proposed method will be evaluated using both simulated data and real data from industrial systems.

Transfer Learning for Machine Diagnosis and Prognosis

2020-10-04T16:24:31Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Study and develop deep adversarial neural networks (DANN) based methods to detect faults and predict failures in industrial equipment, under transfer learning scenarios.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault Diagnosis, Prognosis
|TimeFrame=Fall 2020
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Zhuang, Fuzhen, et al. "A comprehensive survey on transfer learning." Proceedings of the IEEE (2020).

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model-based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Guo, Liang, et al. "Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data." IEEE Transactions on Industrial Electronics 66.9 (2018): 7316-7325.

[6] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain adaptive transfer learning for fault diagnosis." 2019 Prognostics and System Health Management Conference (PHM-Paris). IEEE, 2019.

[7] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[8] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[9] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural
Information Processing Systems. 2016.

[10] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for remaining useful life prediction based on consensus self-organizing models." Reliability Engineering & System Safety 203 (2020): 107098.

[11] Zheng, Huailiang, et al. "Cross-domain fault diagnosis using knowledge transfer strategy: A review." IEEE Access 7 (2019): 129260-129290.
|Prerequisites=Artificial Intelligence, Data Mining, and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Peyman Mashhadi, Yuantao Fan
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2, 11] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in many fields, especially in the application area of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting faults, predicting failures, and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under changing/evolving environmental conditions and were operated in a variety of ways. This reply on the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns, fault types and deterioration patterns are very challenging. Utilizing available data (including fault and failure cases) from other equipment that shares a similar mechanical structure and/or being operated in a similar way with adaptation will be helpful if done properly.

Therefore, machinery diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform feature-representation-based TL (or domain adaptation) methods, for transferring knowledge between tasks, e.g. [6], and/or dealing with new conditions/faults, e.g. [10]. Feature-representation-based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time. Domain Adversarial Neural Networks (DANN) [3, 9] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 7, 8]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression tasks.

The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, etc., to perform machine diagnosis and prognosis, under transfer learning scenarios. The proposed method will be evaluated using both simulated data and real data from industrial systems.

Transfer Learning for Machine Diagnosis and Prognosis

2020-10-04T16:24:03Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Study and develop deep adversarial neural networks (DANN) based methods to detect faults and predict failures in industrial equipment, under transfer learning scenarios.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault Diagnosis, Prognosis
|TimeFrame=Fall 2020
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.
[2] Zhuang, Fuzhen, et al. "A comprehensive survey on transfer learning." Proceedings of the IEEE (2020).
[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).
[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model-based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.
[5] Guo, Liang, et al. "Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data." IEEE Transactions on Industrial Electronics 66.9 (2018): 7316-7325.
[6] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain adaptive transfer learning for fault diagnosis." 2019 Prognostics and System Health Management Conference (PHM-Paris). IEEE, 2019.
[7] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).
[8] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).
[9] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.
[10] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for remaining useful life prediction based on consensus self-organizing models." Reliability Engineering & System Safety 203 (2020): 107098.
[11] Zheng, Huailiang, et al. "Cross-domain fault diagnosis using knowledge transfer strategy: A review." IEEE Access 7 (2019): 129260-129290.
|Prerequisites=Artificial Intelligence, Data Mining, and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Peyman Mashhadi, Yuantao Fan
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2, 11] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in many fields, especially in the application area of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting faults, predicting failures, and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under changing/evolving environmental conditions and were operated in a variety of ways. This reply on the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns, fault types and deterioration patterns are very challenging. Utilizing available data (including fault and failure cases) from other equipment that shares a similar mechanical structure and/or being operated in a similar way with adaptation will be helpful if done properly.

Therefore, machinery diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform feature-representation-based TL (or domain adaptation) methods, for transferring knowledge between tasks, e.g. [6], and/or dealing with new conditions/faults, e.g. [10]. Feature-representation-based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time. Domain Adversarial Neural Networks (DANN) [3, 9] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 7, 8]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression tasks.

The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, etc., to perform machine diagnosis and prognosis, under transfer learning scenarios. The proposed method will be evaluated using both simulated data and real data from industrial systems.

Transfer Learning for Machine Diagnosis and Prognosis

2020-10-04T16:23:21Z

YuantaoFan: Created page with "{{StudentProjectTemplate |Summary=Study and develop deep adversarial neural networks (DANN) based methods to detect faults and predict failures and estimate the health status ..."

{{StudentProjectTemplate
|Summary=Study and develop deep adversarial neural networks (DANN) based methods to detect faults and predict failures and estimate the health status of industrial equipment, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault Diagnosis, Prognosis
|TimeFrame=Fall 2020
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.
[2] Zhuang, Fuzhen, et al. "A comprehensive survey on transfer learning." Proceedings of the IEEE (2020).
[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).
[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model-based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.
[5] Guo, Liang, et al. "Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data." IEEE Transactions on Industrial Electronics 66.9 (2018): 7316-7325.
[6] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain adaptive transfer learning for fault diagnosis." 2019 Prognostics and System Health Management Conference (PHM-Paris). IEEE, 2019.
[7] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).
[8] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).
[9] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.
[10] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for remaining useful life prediction based on consensus self-organizing models." Reliability Engineering & System Safety 203 (2020): 107098.
[11] Zheng, Huailiang, et al. "Cross-domain fault diagnosis using knowledge transfer strategy: A review." IEEE Access 7 (2019): 129260-129290.
|Prerequisites=Artificial Intelligence, Data Mining, and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Peyman Mashhadi, Yuantao Fan
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2, 11] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in many fields, especially in the application area of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting faults, predicting failures, and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under changing/evolving environmental conditions and were operated in a variety of ways. This reply on the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns, fault types and deterioration patterns are very challenging. Utilizing available data (including fault and failure cases) from other equipment that shares a similar mechanical structure and/or being operated in a similar way with adaptation will be helpful if done properly.

Therefore, machinery diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform feature-representation-based TL (or domain adaptation) methods, for transferring knowledge between tasks, e.g. [6], and/or dealing with new conditions/faults, e.g. [10]. Feature-representation-based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time. Domain Adversarial Neural Networks (DANN) [3, 9] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 7, 8]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression tasks.

The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, etc., to perform machine diagnosis and prognosis, under transfer learning scenarios. The proposed method will be evaluated using both simulated data and real data from industrial systems.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-14T09:39:07Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a deep adversarial neural networks (DANN) based method to predict failures and estimate machine health, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.

[9] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models." arXiv preprint arXiv:1909.07053 (2019).
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan, Mohammad Ghaith Altarabichi, Sepideh Pashami,
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machinery diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform TL, using feature-representation based TL or domain adaptation methods, for transferring knowledge between tasks, e.g. [5], and/or dealing with new conditions/faults, e.g. [9]. Feature-representation based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time.

Recently, Domain Adversarial Neural Networks (DANN) [3, 7, 8] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 9]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression task. DANN includes a deep feature extractor and a label predictor, which is a standard architecture for performing supervised learning. The unsupervised domain adaptation task is carried out by a domain classifier, which backpropagates gradients for making features domain-invariant. The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, to perform machine diagnostics and prognostics, using multivariate time series data. The proposed method will be evaluated using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T14:20:27Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a deep adversarial neural networks (DANN) based method to predict failures and estimate machine health, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.

[9] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models." arXiv preprint arXiv:1909.07053 (2019).
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan, Mohammad Ghaith Altarabichi, Sepideh Pashami,
|Level=Master
|Status=Draft
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform TL, using feature-representation based TL or domain adaptation methods, for transferring knowledge between tasks, e.g. [5], and/or dealing with new conditions/faults, e.g. [9]. Feature-representation based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time.

Recently, Domain Adversarial Neural Networks (DANN) [3, 7, 8] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 9]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression task. DANN includes a deep feature extractor and a label predictor, which is a standard architecture for performing supervised learning. The unsupervised domain adaptation task is carried out by a domain classifier, which backpropagates gradients for making features domain-invariant. The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, to perform machine diagnostics and prognostics, using multivariate time series data. The proposed method will be evaluated using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T11:19:17Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a deep adversarial neural networks (DANN) based method to predict failures and estimate machine health, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.

[9] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models." arXiv preprint arXiv:1909.07053 (2019).
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan, Sepideh Pashami, Sławomir Nowaczyk,
|Level=Master
|Status=Draft
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform TL, using feature-representation based TL or domain adaptation methods, for transferring knowledge between tasks, e.g. [5], and/or dealing with new conditions/faults, e.g. [9]. Feature-representation based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time.

Recently, Domain Adversarial Neural Networks (DANN) [3, 7, 8] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 9]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression task. DANN includes a deep feature extractor and a label predictor, which is a standard architecture for performing supervised learning. The unsupervised domain adaptation task is carried out by a domain classifier, which backpropagates gradients for making features domain-invariant. The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, to perform machine diagnostics and prognostics, using multivariate time series data. The proposed method will be evaluated using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T11:18:35Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a deep adversarial neural networks (DANN) based method to predict failures and estimate machine health, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.

[9] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models." arXiv preprint arXiv:1909.07053 (2019).
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan, Sepideh Pashami, Sławomir Nowaczyk,
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform TL, using feature-representation based TL or domain adaptation methods, for transferring knowledge between tasks, e.g. [5], and/or dealing with new conditions/faults, e.g. [9]. Feature-representation based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time.

Recently, Domain Adversarial Neural Networks (DANN) [3, 7, 8] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 9]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression task. DANN includes a deep feature extractor and a label predictor, which is a standard architecture for performing supervised learning. The unsupervised domain adaptation task is carried out by a domain classifier, which backpropagates gradients for making features domain-invariant. The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, to perform machine diagnostics and prognostics, using multivariate time series data. The proposed method will be evaluated using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T11:13:36Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a deep adversarial neural networks (DANN) based method to predict failures and estimate machine health using multivariate time series data, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.

[9] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models." arXiv preprint arXiv:1909.07053 (2019).
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan, Sepideh Pashami, Sławomir Nowaczyk,
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform TL, using feature-representation based TL or domain adaptation methods, for transferring knowledge between tasks, e.g. [5], and/or dealing with new conditions/faults, e.g. [9]. Feature-representation based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time.

Recently, Domain Adversarial Neural Networks (DANN) [3, 7, 8] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 9]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression task. DANN includes a deep feature extractor and a label predictor, which is a standard architecture for performing supervised learning. The unsupervised domain adaptation task is carried out by a domain classifier, which backpropagates gradients for making features domain-invariant. The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, to perform machine diagnostics and prognostics, using multivariate time series data. The proposed method will be evaluated using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T11:13:02Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a deep adversarial neural networks (DANN) based method to predict failures and estimate machine health using multivariate time series data, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.

[9] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models." arXiv preprint arXiv:1909.07053 (2019).
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan, Sepideh Pashami
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform TL, using feature-representation based TL or domain adaptation methods, for transferring knowledge between tasks, e.g. [5], and/or dealing with new conditions/faults, e.g. [9]. Feature-representation based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time.

Recently, Domain Adversarial Neural Networks (DANN) [3, 7, 8] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 9]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression task. DANN includes a deep feature extractor and a label predictor, which is a standard architecture for performing supervised learning. The unsupervised domain adaptation task is carried out by a domain classifier, which backpropagates gradients for making features domain-invariant. The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, to perform machine diagnostics and prognostics, using multivariate time series data. The proposed method will be evaluated using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T11:12:29Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a deep adversarial neural networks (DANN) based method to predict failures and estimate machine health using multi-variate time series data, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.

[9] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models." arXiv preprint arXiv:1909.07053 (2019).
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan, Sepideh Pashami
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform TL, using feature-representation based TL or domain adaptation methods, for transferring knowledge between tasks, e.g. [5], and/or dealing with new conditions/faults, e.g. [9]. Feature-representation based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time.

Recently, Domain Adversarial Neural Networks (DANN) [3, 7, 8] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 9]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression task. DANN includes a deep feature extractor and a label predictor, which is a standard architecture for performing supervised learning. The unsupervised domain adaptation task is carried out by a domain classifier, which backpropagates gradients for making features domain-invariant. The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, to perform machine diagnostics and prognostics, using multivariate time series data. The proposed method will be evaluated using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T11:11:31Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a deep adversarial neural networks (DANN) based method to predict failures and estimate machine health using muti-variate time series data, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.

[9] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models." arXiv preprint arXiv:1909.07053 (2019).
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan, Sepideh Pashami
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform TL, using feature-representation based TL or domain adaptation methods, for transferring knowledge between tasks, e.g. [5], and/or dealing with new conditions/faults, e.g. [9]. Feature-representation based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time.

Recently, Domain Adversarial Neural Networks (DANN) [3, 7, 8] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 9]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression task. DANN includes a deep feature extractor and a label predictor, which is a standard architecture for performing supervised learning. The unsupervised domain adaptation task is carried out by a domain classifier, which backpropagates gradients for making features domain-invariant. The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, to perform machine diagnostics and prognostics, using multivariate time series data. The proposed method will be evaluated using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T11:10:35Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a deep adversarial neural networks (DANN) based method to predict failures and estimate machine health using muti-variate time series data, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.

[9] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models." arXiv preprint arXiv:1909.07053 (2019).
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan,
|Level=Master
|Status=Open
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform TL, using feature-representation based TL or domain adaptation methods, for transferring knowledge between tasks, e.g. [5], and/or dealing with new conditions/faults, e.g. [9]. Feature-representation based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time.

Recently, Domain Adversarial Neural Networks (DANN) [3, 7, 8] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 9]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression task. DANN includes a deep feature extractor and a label predictor, which is a standard architecture for performing supervised learning. The unsupervised domain adaptation task is carried out by a domain classifier, which backpropagates gradients for making features domain-invariant. The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, to perform machine diagnostics and prognostics, using multivariate time series data. The proposed method will be evaluated using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T11:08:52Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a deep adversarial neural networks (DANN) based method to predict failures and estimate machine health using muti-variate time series data, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.

[9] Fan, Yuantao, Sławomir Nowaczyk, and Thorsteinn Rögnvaldsson. "Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models." arXiv preprint arXiv:1909.07053 (2019).
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan,
|Level=Master
|Status=Draft
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform TL, using feature-representation based TL or domain adaptation methods, for transferring knowledge between tasks, e.g. [5], and/or dealing with new conditions/faults, e.g. [9]. Feature-representation based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time.

Recently, Domain Adversarial Neural Networks (DANN) [3, 7, 8] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 9]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression task. DANN includes a deep feature extractor and a label predictor, which is a standard architecture for performing supervised learning. The unsupervised domain adaptation task is carried out by a domain classifier, which backpropagates gradients for making features domain-invariant. The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, to perform machine diagnostics and prognostics, using multivariate time series data. The proposed method will be evaluated using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T11:08:18Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a deep adversarial neural networks (DANN) based method to predict failures and estimate machine health using muti-variate time series data, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan,
|Level=Master
|Status=Draft
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow a very similar distribution is unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnosis and prognosis need to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A suitable solution is to perform TL, using feature-representation based TL or domain adaptation methods, for transferring knowledge between tasks, e.g. [5], and/or dealing with new conditions/faults, e.g. [9]. Feature-representation based TL and domain adaptation methods aim at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. The discrepancy of marginal distributions between the source and the target data in the latent feature space is expected to be reduced at the same time.

Recently, Domain Adversarial Neural Networks (DANN) [3, 7, 8] has been applied for fault diagnosis and machine prognosis [4, 5, 6, 9]. The idea is to train a deep neural network for extracting domain-invariant features that has predictive power for the classification/regression task. DANN includes a deep feature extractor and a label predictor, which is a standard architecture for performing supervised learning. The unsupervised domain adaptation task is carried out by a domain classifier, which backpropagates gradients for making features domain-invariant. The main objective of this work is to develop a DANN based method, e.g. designing network structure and cost functions, to perform machine diagnostics and prognostics, using multivariate time series data. The proposed method will be evaluated using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T09:19:54Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a deep adversarial neural networks (DANN) based method to predict failures and estimate machine health using muti-variate time series data, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan,
|Level=Master
|Status=Draft
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow very similar distributions are unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnostic and prognostic methods needed: (i) adapting to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learning to utilize and transfer knowledge gained from operations of similar equipment/assets. A very popular approach for solving these two problems is Feature-Representation based TL. The feature-representation based TL, e.g. domain adaptation, aims at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. At the same time is the difference in the marginal distribution between the source and the target domain in the latent feature space reduced. Recently, Domain Adversarial Neural Networks (DANN) [3-8] have emerged for performing domain adaptation. The main objective of this work is to develop DANN based models to perform machine diagnostics and prognostics, design network structure and propose cost functions. The proposed methods will be evaluated using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T01:02:48Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Develop a deep adversarial neural networks (DANN) based approach to predict failures and estimate machine health using muti-variate time series data, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan,
|Level=Master
|Status=Draft
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow very similar distributions are unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnostic and prognostic methods needed: (i) adapting to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learning to utilize and transfer knowledge gained from operations of similar equipment/assets. A very popular approach for solving these two problems is Feature-Representation based TL. The feature-representation based TL, e.g. domain adaptation, aims at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. At the same time is the difference in the marginal distribution between the source and the target domain in the latent feature space reduced. Recently, Domain Adversarial Neural Networks (DANN) [3-8] have emerged for performing domain adaptation. The main objective of this work is to develop DANN based models to perform machine diagnostics and prognostics, propose cost functions and design NN structure. The proposed methods will be tested using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T00:59:50Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Design a deep adversarial neural networks (DANN) based approach to predict failures and estimate machine health using muti-variate time series data, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan,
|Level=Master
|Status=Draft
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow very similar distributions are unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnostic and prognostic methods needed: (i) adapting to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learning to utilize and transfer knowledge gained from operations of similar equipment/assets. A very popular approach for solving these two problems is Feature-Representation based TL. The feature-representation based TL, e.g. domain adaptation, aims at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. At the same time is the difference in the marginal distribution between the source and the target domain in the latent feature space reduced. Recently, Domain Adversarial Neural Networks (DANN) [3-8] have emerged for performing domain adaptation. The main objective of this work is to develop DANN based models to perform machine diagnostics and prognostics, propose cost functions and design NN structure. The proposed methods will be tested using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T00:55:36Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=In this project you will use Deep Adversarial Neural Networks to predict failures and estimate machine health using Muti-variate time series data, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan,
|Level=Master
|Status=Draft
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow very similar distributions are unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnostic and prognostic methods needed: (i) adapting to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learning to utilize and transfer knowledge gained from operations of similar equipment/assets. A very popular approach for solving these two problems is Feature-Representation based TL. The feature-representation based TL, e.g. domain adaptation, aims at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. At the same time is the difference in the marginal distribution between the source and the target domain in the latent feature space reduced. Recently, Domain Adversarial Neural Networks (DANN) [3-8] have emerged for performing domain adaptation. The main objective of this work is to develop DANN based models to perform machine diagnostics and prognostics, propose cost functions and design NN structure. The proposed methods will be tested using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T00:54:50Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=In this project you will use Deep (Adversarial) Neural Networks based models to predict failures and estimate machine health using Muti-variate time series data, under transfer learning settings.
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan,
|Level=Master
|Status=Draft
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow very similar distributions are unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnostic and prognostic methods needed: (i) adapting to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learning to utilize and transfer knowledge gained from operations of similar equipment/assets. A very popular approach for solving these two problems is Feature-Representation based TL. The feature-representation based TL, e.g. domain adaptation, aims at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. At the same time is the difference in the marginal distribution between the source and the target domain in the latent feature space reduced. Recently, Domain Adversarial Neural Networks (DANN) [3-8] have emerged for performing domain adaptation. The main objective of this work is to develop DANN based models to perform machine diagnostics and prognostics, propose cost functions and design NN structure. The proposed methods will be tested using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T00:38:39Z

YuantaoFan:

{{StudentProjectTemplate
|Summary=Propose and optimise Deep (Adversarial) Neural Networks based models for machine diagnostics and prognostics under inductive and transductive transfer learning settings
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan,
|Level=Master
|Status=Draft
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow very similar distributions are unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnostic and prognostic methods needed: (i) adapting to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learning to utilize and transfer knowledge gained from operations of similar equipment/assets. A very popular approach for solving these two problems is Feature-Representation based TL. The feature-representation based TL, e.g. domain adaptation, aims at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. At the same time is the difference in the marginal distribution between the source and the target domain in the latent feature space reduced. Recently, Domain Adversarial Neural Networks (DANN) [3-8] have emerged for performing domain adaptation. The main objective of this work is to develop DANN based models to perform machine diagnostics and prognostics, propose cost functions and design NN structure. The proposed methods will be tested using both simulated data and real data coming from heaving duty vehicles.

Transfer Learning for Machine Diagnostics and Prognostics

2019-10-02T00:34:38Z

YuantaoFan: Created page with "{{StudentProjectTemplate |Summary=Propose and optimise Deep (Adversarial) Neural Networks based models for machine diagnostics and prognostics under inductive and transductive..."

{{StudentProjectTemplate
|Summary=Propose and optimise Deep (Adversarial) Neural Networks based models for machine diagnostics and prognostics under inductive and transductive transfer learning settings
|Keywords=Transfer Learning, Domain adaptation, Domain Adversarial Neural Networks, Fault diagnosis, Prognostics
|TimeFrame=Fall 2019
|References=[1] Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

[2] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 9.

[3] Ganin, Yaroslav, and Victor Lempitsky. "Unsupervised domain adaptation by backpropagation." arXiv preprint arXiv:1409.7495 (2014).

[4] W. Lu, B. Liang, Y. Cheng, D. Meng, J. Yang, T. Zhang, Deep model based domain adaptation for fault diagnosis, IEEE Transactions on Industrial Electronics 64 (2017) 2296–2305.

[5] Wang, Qin, Gabriel Michau, and Olga Fink. "Domain Adaptive Transfer Learning for Fault Diagnosis." arXiv preprint arXiv:1905.06004 (2019).

[6] Da Costa, Paulo R. de O., et al. "Remaining useful lifetime prediction via deep domain adaptation." arXiv preprint arXiv:1907.07480 (2019).

[7] Akuzawa, Kei, Yusuke Iwasawa, and Yutaka Matsuo. "Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization." arXiv preprint arXiv:1904.12543 (2019).

[8] Long, Mingsheng, et al. "Unsupervised domain adaptation with residual transfer networks." Advances in Neural Information Processing Systems. 2016.
|Prerequisites=Artificial Intelligence and Learning Systems courses; good knowledge of machine learning and neural networks; programming skills for implementing machine learning algorithms
|Supervisor=Yuantao Fan,
|Level=Master
|Status=Draft
}}
Transfer Learning (TL) [1, 2] refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain) and it is needed in the field of machine diagnostics and prognostics. The current industrial approach for developing machine diagnostic and prognostic methods usually relies on data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this paradigm assumes that future field data will have a very similar distribution to the experiment data. However, many machines were operated under dynamic environmental conditions and were operated in a variety of ways. This makes the assumption that pre-deployment data and post-deployment data follow very similar distributions are unlikely to hold. Moreover, since industrial systems are not allowed to run until failure (for safety reasons), collecting data that has comprehensive coverage on various usages patterns and deterioration patterns is very challenging.

Therefore, machine diagnostic and prognostic methods needed to: (i) adapt to more complex scenarios where unseen degradation patterns and new operating conditions are present (in the testing data); (ii) learn to utilize and transfer knowledge gained from operations of similar equipment/assets. A very popular approach for solving these two issues is Feature-Representation based TL. The feature-representation based TL, e.g. domain adaptation, aims at discovering meaningful common structures between the source and the target domain, finding transformations that project the source data and the target data into a common latent feature space, which has predictive qualities for solving the target task. At the same time is the difference in the marginal distribution between the source and the target domain in the latent feature space reduced. Recently, Domain Adversarial Neural Networks (DANN) [3-8] have emerged for performing domain adaptation. The main objective of this work is to develop DANN based models to perform machine diagnostics and prognostics, propose cost functions and design NN structure. The proposed methods will be tested using both simulated data and real data coming from heaving duty vehicles.