An improved fault diagnosis method based on deep wavelet neural network

Deep learning has been successfully applied to the field of fault diagnosis in recent years. Due to the advantages of deep belief network (DBN) in fitting nonlinear complex systems and the ability of wavelet analysis in time-frequency analysis, in this paper, an improved fault diagnosis method based on a deep wavelet neural network (DWNN), which combines the DBN with morlet activation functions, is proposed for fault diagnosis of reciprocating compressor. A five-layer DBN using sigmoid, tanh, rectified linear unit (ReLU) and morlet wavelet functions as the activation functions of hidden layers separately is proposed for fault diagnosis of reciprocating compressor. As the contrast, a three-layer back propagation neural network (BPNN) using the same four activation functions separately is proposed for fault diagnosis of reciprocating compressor. The experimental results show that, the fault diagnosis rate of five-layer DBN is higher than the three-layer BPNN. The method based on DWNN can make the fault diagnosis rate reach 100% within short time. Compared with using other activation functions, the DWNN architecture requires less epochs to train the model.