Fault diagnosis of subway auxiliary inverter based on EEMD and GABP

Focusing on the non-stationary characteristic of the fault signal of subway auxiliary inverter, this paper proposes the method that combines ensemble empirical mode decomposition (EEMD) with genetic algorithm to optimize BP neural network (GABP) to diagnose the fault categories of subway auxiliary inverter. Firstly, this paper extracts feature vectors from the original fault signal by EEMD, then establishes the multi-fault diagnosis model by GABP. The genetic algorithm (GA) is introduced to search the optimal solutions of initial weight and thresholds of BP neural network (BPNN), so as to improve the convergence and precision of diagnosis of network. Simulation results show that this method we proposed can identify these faults more accurately and higher efficiently.