Slipping Detection of Electric Locomotive Based on Empirical Wavelet Transform, Fuzzy Entropy Algorithm and Support Vector Machine

Abstract

During the running of electric locomotives, a large amount of operating data will be stored through the Analog-Digital Converter (ADC) and the recording oscillograph of the on-board Traction Control Unit (TCU). Analysis based on mass data to detect slipping is of great significance for saving energy, improving the performance and safety of the traction control system of electric locomotives. Therefore, after analyzing and summarizing the advantages of Empirical Wavelet Transform (EWT) and Fuzzy Entropy (FE) algorithm in data processing and the classification theory of Support Vector Machine (SVM), this paper proposed a SVM slipping detection method based on EWT and FE algorithm. Firstly, feature extraction is carried out on the locomotive wheelset velocity data collected by the on-board velocity sensor, that is, the feature vector of data is extracted by calculating fuzzy entropy value after EWT, and the velocity data is converted into feature matrix for slipping-detection of SVM. Then considering the defects of traditional optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the parameter optimization of SVM, Genetic Particle Swarm Optimization Algorithm (GAPSO) is proposed to optimize the parameters of SVM classification model, and the slipping detection model of SVM is established through training samples, which realizes high-precision slipping detection. Finally, combined with a large number of locomotive data, the comparative analysis test is carried out, the results show that the feature extraction method based on EWT and FE algorithm has excellent ability of mode decomposition and feature extraction, GAPSO has efficient optimization ability, and the proposed slipping detection method can accurately detect slipping.