Fault Diagnosis of Subway Auxiliary Inverter Based on PCA and WNN

Abstract

Taken the nonlinearity of fault signals in subway auxiliary inverter and the diagnostic precision into consideration, the paper proposes the fault diagnosis method on the basis of principal component analysis (PCA) and wavelet neural network (WNN). Firstly, extract the initial feature vectors of fault signals by the decomposition and reconstruction of wavelet package, then use PCA to reduce the dimension of initial feature vectors, so as to eliminate redundant data information. Finally, the processed feature vectors will be taken as the input samples of wavelet neural network for the fault diagnosis. Experiment results have tested and verified the feasibility and effectiveness of the method. The proposed diagnostic method has higher precision and stronger convergence than the network directly using initial feature vectors.