TR2019-057
Fault Detection and Classification of Time Series Using Localized Matrix Profiles
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- , "Fault Detection and Classification of Time Series Using Localized Matrix Profiles", IEEE International Conference on Prognostics and Health Management, DOI: 10.1109/ICPHM.2019.8819389, June 2019.BibTeX TR2019-057 PDF
- @inproceedings{Zhang2019jun,
- author = {Zhang, Jing and Nikovski, Daniel N. and Lee, Teng-Yok and Fujino, Tomoya},
- title = {Fault Detection and Classification of Time Series Using Localized Matrix Profiles},
- booktitle = {IEEE International Conference on Prognostics and Health Management},
- year = 2019,
- month = jun,
- doi = {10.1109/ICPHM.2019.8819389},
- url = {https://www.merl.com/publications/TR2019-057}
- }
- , "Fault Detection and Classification of Time Series Using Localized Matrix Profiles", IEEE International Conference on Prognostics and Health Management, DOI: 10.1109/ICPHM.2019.8819389, June 2019.
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Abstract:
We introduce a new primitive, called the Localized Matrix Profile (LMP), for time series data mining. We devise fast algorithms for LMP computation, and propose a fault detector and a fault classifier based on the LMP. A case study using synthetic sensor data generated from a physical model of an electrical motor is provided to demonstrate the effectiveness and efficiency of our approach.