Research and Implementation of Fault Diagnosis of Switch Machine Based on Data Enhancement and CNN

Abstract

Fault detection of point machine operations is discussed in this paper, which is critical for ensuring the safety of a running train.This paper adopts the method of data enhancement and convolutional neural network (CNN) to study and realize the fault diagnosis of power data of switch switch machine. Firstly, six typical fault types and possible fault causes are summarized by analyzing the working process of switch machine and its power curve characteristics. In view of the imbalance of switch data, the synthesized minority oversampling technique (SMOTE) is implemented to generate switch fault data and balance switch data set. In view of the low accuracy of turnout fault diagnosis, one-dimensional convolutional neural network is adopted to classify the turnout fault diagnosis model, which further improves the accuracy of turnout fault diagnosis model and provides theoretical support for railway field maintenance. To a certain extent, it overcomes the difficulties of instability and low efficiency of manual turnout fault detection method.