Open AccessFault diagnosis of railway point machines using dynamic time warping
Author(s) -
Kim H.,
Sa J.,
Chung Y.,
Park D.,
Yoon S.
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.0206
Subject(s) - dynamic time warping , image warping , fault (geology) , classifier (uml) , computer science , engineering , training set , point (geometry) , pattern recognition (psychology) , artificial intelligence , mathematics , geometry , seismology , geology
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.