Research on Fault Diagnosis Method of Rotating Machinery Based on Deep Learning

Based on the deficiency in the traditional fault diagnosis method, i.e shallow learning is usually used to characterize complex mapping relationship between vibration signals and the rotor system, a deep neural network (DNN)based on denoising auto-encoder is proposed. At the same time, the proposed method has been successfully applied to the fault diagnosis of rotating machinery. In the proposed method, the frequency domain information of the vibration signal is used as input signal, and the deep neural network is obtained by the unsupervised training of the denoising auto encoder, the dropout method is employed to adjust the network parameters, reducing the problem of the over-fitting. Finally, the fault features obtained by learning of denoising auto-encoder is used for fault diagnosis. The experimental result show that the proposed method is very effective, and can effectively extract the implicit characteristics of the fault signal.