A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
Abstract
Identifying the change point of a system's health status is important. Indeed, a change point usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection that could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status. In this paper, we propose a novel approach for calibrating OC-SVM models. Our approach uses a heuristic search method to find a good set of input data and hyperparameters that yield a well-performing model. Our results on the C-MAPSS dataset demonstrate that OC-SVM can achieve satisfactory accuracy in detecting change point in time series with fewer training data, compared to state-of-the-art deep learning approaches. In our case study, the OC-SVM calibrated by the proposed model is shown to be useful especially in scenarios with limited amount of training data.
Additional Information
© 2019 IEEE. This work is supported in part by the National Research Foundation of Singapore through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) program, and by the National Science Foundation under Grant No. 1645964.Attached Files
Submitted - 1902.06361.pdf
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Additional details
- Eprint ID
- 98465
- DOI
- 10.1109/ICPHM.2019.8819385
- Resolver ID
- CaltechAUTHORS:20190905-160001172
- National Research Foundation (Singapore)
- CNS-1645964
- NSF
- Created
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2019-09-05Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field