Quarterly Report of RTRI
Online ISSN : 1880-1765
Print ISSN : 0033-9008
ISSN-L : 0033-9008
PAPERS
Anomaly Detection for Railway Vehicle Equipment Using Condition Monitoring Data
Toshihide YOKOUCHITatsuro TAKASHIGEMinoru KONDO
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2022 Volume 63 Issue 4 Pages 238-243

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Abstract

In recent years, some railway vehicles have been equipped with condition monitoring devices, which constantly record the operating condition of railway vehicle equipment. For more effective use of condition monitoring devices, we propose an anomaly detection method for railway vehicle equipment using Long Short-Term Memory (LSTM), which is a deep learning method suitable for learning time-series data. In this paper, we apply the proposed method to data on engines and air-conditioning units recorded on vehicles in operations. Results confirmed that the anomaly score for anomalous data increases by using the proposed method, and that anomalies are detected in railway vehicle equipment before faults appear.

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© 2022 by Railway Technical Research Institute
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