Fault Diagnosis of Traction Inverter Based on Wavelet Analysis and Neural Network

Abstract

Due to IGBT open-circuit fault of CRH2 EMU’s traction inverter, a method of its fault diagnosis with the three-phase current signals as detection objects is conducted. By applying the wavelet analysis, three-phase current signals are decomposed for four times. With the coefficients of each layer obtained, the energy values of layers are calculated as well as the vectors corresponding to failure modes. According to the vectors regarded as input and the expected output, a BP neural network is established. Through training the network, the parameters of network can be defined. In addition, to test and evaluate the performance of network, certain noise is added to the three-phase current signals. Simulation results show it is feasible for the fault diagnosis of traction inverter.