Hybrid fault diagnosis of railway switches based on the segmentation of monitoring curves

Abstract

Switches are one of the most important pieces of infrastructure in railway signal systems, and they significantly influence the efficiency and safety of train operation. Currently, the identification of switch failures mainly depends on the experience of railway staff and the use of simple thresholding methods. However, these basic methods are highly inaccurate and frequently result in false and missing alarms. This paper aims to develop a hybrid fault diagnosis (HFD) method for railway switches. The method is an intelligent diagnosis method that uses massive current curves collected by microcomputer monitoring systems. We first divide the switch operation current curves into three segments based on the three mechanical processes that occur during switch operation. Then, a standard curve is selected from the fault-free curves, and common typical faults are ascertained through a microcomputer monitoring system. Finally, derivative dynamic time warping 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 HFD method is extremely accurate and has low false and missing alarm rates. HFD performs better than the studied support vector machine (SVM) and dynamic time warping (DTW) methods, which are widely used for fault diagnosis.

On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview

“…An FD technique only recognizes that a problem has occurred in the system, but it does not identify the root causes nor isolate the location of different faults (i.e., there is no isolation step). Thus, many researchers have developed different FDD techniques to deal with railway S&C system CM, and many papers can be found [11,13,15,28,34,[100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115][116][117]; only selected papers are discussed in the following. It should be clearly noted that the only criteria applied for this selection were that only the most recent published papers were chosen (from 2017 onwards).…”

“…In addition, they mentioned that their method will be suited to real-time CM since it does not need feature selection, historical data, or prior knowledge. Ou et al [115] proposed a hybrid FDD method for dealing with the CM of a railway S&C system. The hybrid FDD method was based on derivative DTW and a quartile scheme.…”

“…FDD methods [11,13,15,28,34,[100][101][102][103][104][105][106][107][108][109][110][111][112][113][114][115][116][117]120] PCA [102,117]; SVM [11,104,105,110]; SVM combined with other algorithms (e.g., PCA, envelope, etc.) [15,101,117]; wavelet transform (WT) and its variants (e.g., CWT and discrete WT (DWT)) [15,101]; DTW [111,112,114,115]; SOM [108]; backpropagation (BP) neural network (NN) [107]; probabilistic NN (PNN) [106]; 3D reconstruction [113]; k-nearest neighbor method [100]; fuzzy c-mean [116]; qualitative trend analysis (QTA) [13]; ANN [103]; BLT and NN…”

“…Voltage [15,100,101,104,108]; force [13,15,34,[100][101][102]120]; current [13,15,28,[100][101][102][103][105][106][107][108]110,111,[114][115][116][117]; power [11,34,117,120]; strain signal [110,113]; switch displacement [13]; linear position of the switch rails [104]; motor temperature [108]; audio signal (sound sensor) [117]; 2D measurements [113]; speed [120].…”

“…Wear in crossing [113]; switch-related faults [105][106][107][110][111][112]114,117]; slide chair-related faults [109,117]; startup circuit disconnection (bad contact) [106,107,114,115,117]; actuator is not flexible [114,115]; abnormal fluctuations [115]; electric relay switch failure [115]; mechanical jam fault [117]; abnormal impedance [117]; lack of motor lubrication [11]; overdriven and underdriven [13,15,34,101]; drive rod out of adjustment [104]; motor deblocking [107]; obstacles (hard or soft) [108,120]; left and right misaligned adjuster bolts [120]; missing bearings [120].…”