Fault diagnosis of railway switch machines based on VMD-SDP-CNN

Abstract

The switch machine is a vital component in the railway system, playing a significant role in ensuring the safe operation of trains. To address the shortcomings of existing fault diagnosis methods for the switch machine and leveraging the strong anti-interference and high sensitivity characteristics of vibration signals, we proposed a VMD-SDP-CNN(Variational mode decomposition-Symmetric dot pattern-Convolutional neural network) fault diagnosis method based on switch machine vibration signals. Firstly, the vibration signal of the switch machine was decomposed by VMD to obtain several intrinsic mode function (IMF) components. Secondly, the SDP method was employed to transform the decomposed IMF components into two-dimensional images, and the issue of one-dimensional signal recognition was transformed into the issue of two-dimensional image recognition. Finally, a CNN was used to realize the fault diagnosis of the switch machine. The experimental results showed that the recognition accuracy of the five actual working conditions of the switch machine using this method was superior to that of typical deep learning and machine learning methods, verifying its practicability and effectiveness.

Keywords

switch machine / rail transit / turnout / intelligent diagnosis / vibration signal / signal decomposition / deep learning

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