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Open AccessArticle

An Online Classification Method for Fault Diagnosis of Railway Turnouts

by Dongxiu Ou 1, Yuqing Ji 1, Lei Zhang 2,* and Hu Liu 3
1
Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Shanghai 201804, China
2
School of Transportation Engineering, Tongji University, No.4800 Caoan Road, Shanghai 201804, China
3
School of Rail Transit, Shanghai Institute of Technology, No.100 Haiquan Road, Shanghai 201418, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(16), 4627; https://doi.org/10.3390/s20164627
Received: 17 July 2020 / Revised: 11 August 2020 / Accepted: 14 August 2020 / Published: 17 August 2020
Railway turnout system is a key infrastructure to railway safety and efficiency. However, it is prone to failure in the field. Therefore, many railway departments have adopted a monitoring system to monitor the operation status of turnouts. With monitoring data collected, many researchers have proposed different fault-diagnosis methods. However, many of the existing methods cannot realize real-time updating or deal with new fault types. This paper—based on imbalanced data—proposes a Bayes-based online turnout fault-diagnosis method, which realizes incremental learning and scalable fault recognition. First, the basic conceptions of the turnout system are introduced. Next, the feature extraction and processing of the imbalanced monitoring data are introduced. Then, an online diagnosis method based on Bayesian incremental learning and scalable fault recognition is proposed, followed by the experiment with filed data from Guangzhou Railway. The results show that the scalable fault-recognition method can reach an accuracy of 99.11%, and the training time of the Bayesian incremental learning model reduces 29.97% without decreasing the accuracy, which demonstrates the high accuracy, adaptability and efficiency of the proposed model, of great significance for labor-saving, timely maintenance and further, safety and efficiency of railway transportation. View Full-Text
Keywords: turnout system; fault diagnosis; incremental learning; scalable recognition turnout system; fault diagnosis; incremental learning; scalable recognition
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Ou, D.; Ji, Y.; Zhang, L.; Liu, H. An Online Classification Method for Fault Diagnosis of Railway Turnouts. Sensors 2020, 20, 4627.

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