Fault diagnosis of railway point machines using dynamic time warping

Abstract

A practical condition monitoring method is proposed for the fault diagnosis of railway point machines (RPMs) by considering the difficulty of obtaining in-field failure data. Failures in RPMs have a significant effect on railway train operations, and it is very crucial to detect abnormal conditions in RPMs. However, it is generally difficult to obtain in-field failure data for a classifier training step. A diagnosis method using dynamic time warping is proposed to manage the variation in durations of RPM movement without a training step. On the basis of the experimental results with RPMs operated in Korea, it is believed that the proposed method without a training step can detect abnormal electric-current shapes more accurately than previous training-based methods.