A feature learning method for rotating machinery fault diagnosis via mixed pooling deep belief network and wavelet transform

Deep learning has extensive application in fault diagnosis regarding the health monitoring of machinery core components. Although deep networks have better nonlinear representation ability, they inevitably introduce a large number of parameters, resulting in slow model training, as well as poor generalization. Additionally, the compound fault tends to be neglected in conventional fault diagnosis. For this purpose, a bearing fault diagnosis method based on mixed pooling deep belief network (MP-DBN) is suggested. Firstly, the Morlet wavelet is adopted for obtaining the corresponding time–frequency representation to enhance analysis efficiency. Then, a new MP-DBN model with a mixed pooling layer and restricted Boltzmann machines (RBM) is constructed to reduce the effects of overfitting and the parameters scale. Finally, the MP-DBN-based method is employed for the bearings in the laboratory and actual working circumstances to verify its performance. The results show that MP-DBN is a powerful compound fault diagnosis technique for rolling bearings with superior feature extraction ability and diagnosis efficiency