Analysis of industrial time series - Revision history

Islab at 11:54, 4 October 2022

2022-10-04T11:54:39Z

Islab at 16:04, 20 September 2022

2022-09-20T16:04:30Z

Islab: Created page with "{{StudentProjectTemplate |Summary=studying the recent advances in time series forecasting and their application in modelling time series of Alfa Laval's industrial machines |..."

2022-09-19T12:44:43Z

Created page with "{{StudentProjectTemplate |Summary=studying the recent advances in time series forecasting and their application in modelling time series of Alfa Laval's industrial machines |..."

New page

{{StudentProjectTemplate
|Summary=studying the recent advances in time series forecasting and their application in modelling time series of Alfa Laval's industrial machines
|Keywords=time series, machine learning, deep learning, LSTM, transformers, informer
|TimeFrame=ASAP
|References=Zhou, Haoyi, et al. "Informer: Beyond efficient transformer for long sequence time-series forecasting." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 12. 2021.[Best Paper Award]

|Prerequisites=Data Mining Course
|Supervisor=Hadi Fanaee
|Level=Master
|Status=Draft
}}
This is a fantastic opportunity to work with Alfa Laval, a world's leader and pioneer in heat transfer, centrifugal separation and fluid handling. During the project, you will have this opportunity to gain access to real-life industrial IoT data and gain first-hand experience with such kind of valuable data.

This project aims to investigate the application of recent time series models (such as transformers, informer, LSTM, etc.) for modelling sensor time series of industrial machines at Alfa Laval.

The main objective of this project is to evaluate the usefulness of recent techniques in modelling sensor time series. Generating accurate time series models opens new opportunities to develop new-generation of real-time anomaly detection systems and prevention of alarms and warnings. The benefits of more in-depth exploration are both in terms of technical and business value.

← Older revision		Revision as of 11:54, 4 October 2022
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	The main objective of this project is to evaluate the usefulness of recent techniques in modelling sensor time series. Generating accurate time series models opens new opportunities to develop new-generation of real-time anomaly detection systems and prevention of alarms and warnings. The benefits of more in-depth exploration are both in terms of technical and business value.		The main objective of this project is to evaluate the usefulness of recent techniques in modelling sensor time series. Generating accurate time series models opens new opportunities to develop new-generation of real-time anomaly detection systems and prevention of alarms and warnings. The benefits of more in-depth exploration are both in terms of technical and business value.
		+
		+	Detailed discussion is possible at my office (E505) with a previous appointment (hadi.fanaee@hh.se)

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 |TimeFrame=ASAP
 |References=Zhou, Haoyi, et al. "Informer: Beyond efficient transformer for long sequence time-series forecasting." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 12. 2021.[Best Paper Award]
 |Prerequisites=Data Mining Course
 |Supervisor=Hadi Fanaee
 |Level=Master
-|Status=Draft
+|Status=Open
 }}
 This is a fantastic opportunity to work with Alfa Laval, a world's leader and pioneer in heat transfer, centrifugal separation and fluid handling. During the project, you will have this opportunity to gain access to real-life industrial IoT data and gain first-hand experience with such kind of valuable data.