View source for Time series anomaly detection for heavy-duty vehicles

{{StudentProjectTemplate
|Summary=Detecting anomalies in multivariate time series data collected from vehicle operations
|TimeFrame=Fall 2023
|References=Ruiz, C., Menasalvas, E., & Spiliopoulou, M. (2009). C-denstream: Using domain knowledge on a data stream. In Discovery Science: 12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009 12 (pp. 287-301). Springer Berlin Heidelberg.

Cao, F., Estert, M., Qian, W., & Zhou, A. (2006, April). Density-based clustering over an evolving data stream with noise. In Proceedings of the 2006 SIAM international conference on data mining (pp. 328-339). Society for industrial and applied mathematics.

Fan, Y., Nowaczyk, S., & Antonelo, E. A. (2016). Predicting air compressor failures with echo state networks. In PHM Society European Conference (Vol. 3, No. 1).

Fan, Y., Nowaczyk, S., & Rögnvaldsson, T. (2020). Transfer learning for remaining useful life prediction based on consensus self-organizing models. Reliability Engineering & System Safety, 203, 107098.

Ahmad, S., Lavin, A., Purdy, S., & Agha, Z. (2017). Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262, 134-147.

Lavin, A., & Ahmad, S. (2015, December). Evaluating real-time anomaly detection algorithms--the Numenta anomaly benchmark. In 2015 IEEE 14th international conference on machine learning and applications (ICMLA) (pp. 38-44). IEEE.

Wu, K., Zhang, K., Fan, W., Edwards, A., & Philip, S. Y. (2014, December). Rs-forest: A rapid density estimator for streaming anomaly detection. In 2014 IEEE international conference on data mining (pp. 600-609). IEEE.

Hendrickx, K., Meert, W., Mollet, Y., Gyselinck, J., Cornelis, B., Gryllias, K., & Davis, J. (2020). A general anomaly detection framework for fleet-based condition monitoring of machines. Mechanical Systems and Signal Processing, 139, 106585.
|Supervisor=Yuantao Fan, Hamid Sarmadi,
|Status=Internal Draft
}}
This project will explore and develop algorithms to detect anomalies in multi-variate time series data, collected from heavy-duty vehicles. The idea is to identify different operating modes using stream clustering algorithms and discover anomalies in each cluster, i.e. combing DenStream and Consensus self-organizing models methods (COSMO) for anomaly detection. The goal is to discover anomalous events in advance to enhance vehicle operation safety. The project includes: i) proposing a streaming anomaly detection framework; ii) finding suitable data representations that can capture key characteristics in vehicle operation; and iii)  evaluating the proposed approach on a real-world dataset. This work is a collaboration with industry.