Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder

• Published in: Applied sciences 2021-10, Vol.11 (19), p.9290
• Main Authors: Kang, Jaeyong, Kim, Chul-Su, Kang, Jeong Won, Gwak, Jeonghwan
• Format: Article
• Language: English
• Subjects: Algorithms; Anomalies; anomaly detection; brake operating unit; Brakes; Clustering; Datasets; Deep learning; Inspection; Long short-term memory; Machine learning; Pressure; Principal components analysis; signal processing; Support vector machines; Time series
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11199290

Detecting anomalies in the Brake Operating Unit (BOU) braking system of metro trains is very important for trains’ reliability and safety. However, current periodic maintenance and inspection cannot detect anomalies at an early stage. In addition, constructing a stable and accurate anomaly detection system is a very challenging task. Hence, in this work, we propose a method for detecting anomalies of BOU on metro vehicles using a one-class long short-term memory (LSTM) autoencoder. First, we extracted brake cylinder (BC) pressure data from the BOU data since one of the anomaly cases of metro trains is that BC pressure relief time is delayed by 4 s. After that, extracted BC pressure data is split into subsequences which are fed into our proposed one-class LSTM autoencoder which consists of two LSTM blocks (encoder and decoder). The one-class LSTM autoencoder is trained using training data which only consists of normal subsequences. To detect anomalies from test data that contain abnormal subsequences, the mean absolute error (MAE) for each subsequence is calculated. When the error is larger than a predefined threshold which was set to the maximum value of MAE in the training (normal) dataset, we can declare that example an anomaly. We conducted the experiments with the BOU data of metro trains in Korea. Experimental results show that our proposed method can detect anomalies of the BOU data well.