Fault Detection for Subway Auxiliary Inverter Based on EMD and RBFNN
Cheng Lian · Journal of Qingdao University · 2014
Focusing on the non-stationary characteristics of the fault signal of subway auxiliary inverter,this paper proposes a method.by combining empirical mode decomposition(EMD)with radical basis function(RBF)neural network to diagnose fault for metro auxiliary inverter.Empirical mode decomposition method is applied to analyze original non-stationary signal.The original signal is decomposed into several smooth intrinsic mode functions(IMF).The k-means clustering algorithm is used to determine the parameters of RBF neural network model.It can detect the faults of feature vector according to the classified ability of RBF neural network.According to the analysis results of the fault signal of subway auxiliary inverter,the accuracy of this algorithm is higher than the foundational RBF neural network.The results satisfy the requirement of fault diagnosis of subway auxiliary inverter,and identify the fault efficiently.