Fault diagnosis of bearing based on wavelet convolutional autoencoder and LSTM network
                                                   DU Xiaolei1,2, CHEN Zhigang1,2, XU Xu1,2, ZHANG Nan1
(1.School of MechanicalElectronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China; 2.Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing 100044, China)

Abstract: Aiming at the problems that traditional fault diagnosis algorithms of rolling bearings have such shortcomings as largely dependent on expert prior knowledge and difficulty in fault feature extraction, combined with the merits of deep learning in dealing with highdimensional and nonlinear data, a method based on deep wavelet convolutional autoencoder (DWCAE) and long short term memory (LSTM) network was proposed. Firstly, wavelet convolutional autoencoder (WCAE) was designed, and improved loss function and contraction term restriction were introduced to alleviate the overfitting of the network. Secondly, several WCAEs were stacked to construct DWCAE. A large number of unlabeled samples were used for unsupervised pretraining of DWCAE, and the deeper features that were more favorable to fault diagnosis were mined. Finally, LSTM network was trained with deeper features, and the diagnosis model was established. The results of simulation signal and engineering application analysis indicate that the proposed method can effectively identify the bearing faults under multiple working conditions and multiple fault severities. The proposed method has better ability of feature extraction and recognition than traditional methods such as artificial neural network, support vector machine and deep learning methods such as deep belief network, deep autoencoder and so on.

Key words: rolling bearing; fault diagnosis; wavelet convolutional autoencoder; long short term memory (LSTM) network; deep learning