Fault feature selection of Subway Plug Door Based on ReliefF and BGWO

Abstract

In view of the problem that the characteristic extraction of subway plug door fault diagnosis is too high, which leads to low diagnostic accuracy, a mixed feature selection method based on ReliefF algorithm and BGWO (binary grey wolf optimizer, BGWO) was proposed. Firstly, multiple domains feature extraction were carried out on the collected current signal of the subway plug door motor, and an original fault feature set describing the subway plug door fault was obtained. Afterwards, the ReliefF algorithm was used to evaluate the extracted original fault feature weights and screened out the less relevant features. Finally, the classification error rate of GWO (grey wolf optimizer, GWO)-SVM (support vector machine, SVM) is used as the fitness value, and BGWO is used as the feature selection algorithm to perform feature selection on the feature subset obtained by the ReilefF algorithm. The data collected in a Metro Depot in Jiangsu Province is used as the original data set for verification. The experimental results show that the method can screen out low dimensional fault feature sets with high correlation, low redundancy and high fault identification, it can effectively improve the accuracy of subway plug door fault diagnosis.