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A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection

Jin, Baihong and Chen, Yuxin and Li, Dan and Poolla, Kameshwar and Sangiovanni-Vincentelli, Alberto (2019) A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection. In: 2019 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE , Piscataway, NJ, pp. 1-5. ISBN 978-1-5386-8357-6. https://resolver.caltech.edu/CaltechAUTHORS:20190905-160001172

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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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/ICPHM.2019.8819385DOIArticle
https://arxiv.org/abs/1902.06361arXivDiscussion Paper
ORCID:
AuthorORCID
Sangiovanni-Vincentelli, Alberto0000-0003-1298-8389
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.
Funders:
Funding AgencyGrant Number
National Research Foundation (Singapore)UNSPECIFIED
NSFCNS-1645964
Subject Keywords:Support vector machine, change point detection
Record Number:CaltechAUTHORS:20190905-160001172
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190905-160001172
Official Citation:B. Jin, Y. Chen, D. Li, K. Poolla and A. Sangiovanni-Vincentelli, "A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection," 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), San Francisco, CA, USA, 2019, pp. 1-5. doi: 10.1109/ICPHM.2019.8819385
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98465
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:05 Sep 2019 23:06
Last Modified:03 Oct 2019 21:41

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