Entropy Feature Fusion-Based Diagnosis for Railway Point Machines Using Vibration Signals Based on Kernel Principal Component Analysis and Support Vector Machine

Abstract

Railway point machines are the key equipment that controls the train route and affects the safety of train operation. Complex and harsh working environments lead to frequent failures, accounting for 40% of the total failures of the railway signaling system. Thus, it is an urgent task to present an intelligent fault diagnosis approach. Considering the easy acquisition and anti-interference characteristics of vibration signals, this article develops a vibration signal-based diagnosis approach. First, variational mode decomposition (VMD) is utilized for nonstationary vibration signal preprocessing, which is verified as a more effective tool than empirical mode decomposition. Then, to comprehensively characterize nonlinear fault characteristics, five kinds of entropy are extracted. To eliminate the redundant information of high-dimensional features, kernel principal component analysis is utilized for multientropy feature fusion. Experiment comparisons demonstrate the superiority of the proposed VMD preprocessing and multientropy fusion method. The diagnosis accuracies of normal-to-reverse and reverse-to-normal switching directions reach 96.57% and 99.43%, respectively, which provides theoretical support for onsite operation and maintenance staff.