Fault Diagnosis of Traction Converter Based on Improved Multiscale Permutation Entropy and Wavelet Analysis

Abstract

To solve the problems of low fault diagnosis rate and poor efficiency of AC-DC drive traction converter, a fault diagnosis method based on improved multiscale permutation entropy and wavelet analysis is proposed based on the multiple fault characteristics of input current curve in frequency domain. Firstly, the curve of the traction converter is decomposed by wavelet transform, and the modal components of different time scales are obtained. Then the fault characteristic parameters of different components are calculated by improved multi-scale permutation entropy. Finally, the multivariable support vector machine algorithm based on decision tree is used to obtain the tree-like optimal fault interval surface through small sample training, so as to achieve the fault classification of traction converters. The experimental results show that this method can effectively distinguish the fault types of traction converters, and improve the accuracy and efficiency of fault diagnosis, which has good adaptability and practical significance.