A Segmented Similarity-Based Diagnostic Model for Railway Turnout System

Turnouts are one of the most crucial infrastructures in railway signal systems, and they significantly influence the safety and efficiency of train operation. Currently, the identification of turnout failures mainly depends on the experience of railway staff and the use of simple thresholding methods. However, these basic methods are highly defective and frequently result in false and missing alarms. This paper aims to develop a segmented similarity-based diagnostic (SSBD) method for railway turnouts. The intelligent process identifies faults based on current curves collected by microcomputer monitoring systems. The authors first divide these current curves into three segments based on the three mechanical processes that occur during turnout operation. Then, a standard curve is selected from fault-free curves, and common typical faults are ascertained through a microcomputer monitoring system.Finally, Gaussian dynamic time warping (GDTW) and a quartile scheme are employed to identify fault curves. An experiment based on current curves collected from the Guangzhou Railway Bureau in China demonstrates that the SSBD method is extremely accurate and has low missing and false alarm rates. Besides, SSBD performs better than the studied dynamic timing warping (DTW), support vector machine (SVM) and artificial neural network (ANN) methods, which are widely used for fault diagnosis.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AR030 Standing Committee on Railroad Operating Technologies.
  • Corporate Authors:

    Transportation Research Board

    ,    
  • Authors:
    • Ou, Dongxiu
    • Tang, Maojie
    • Xue, Rui
    • Yao, Hongjing
  • Conference:
  • Date: 2019

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 17p

Subject/Index Terms

Filing Info

  • Accession Number: 01697340
  • Record Type: Publication
  • Report/Paper Numbers: 19-00931
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:24AM