关键词: 故障诊断, 深度学习, 改进小波自编码器, 滚动轴承

Abstract:

Aiming at the problems that traditional methods of bearings fault diagnosis heavily depend on expert experience and difficulty in fault feature extraction, combined with the merits of deep learning in dealing with high dimensional and nonlinear data, a novel method based on improved deep wavelet auto-encoder is proposed. Firstly, the loss function of Wavelet Auto-Encoder（WAE） is improved and contractive item constraint is introduced. Secondly, multiple WAEs are stacked to form a Deep Wavelet Auto-Encoder（DWAE） and “cross-layer” connection is introduced to alleviate gradient disappearance. Finally, unsupervised pre-training of DWAE is performed using a large amount of unlabeled data and the model parameters are supervised and fine-tuned with a small amount of tagged data. The bearings diagnosis experimental results show that the method can effectively identify the bearings with multiple fault types and multiple fault severities. The feature extraction ability and recognition ability are superior than Artificial Neural Network（ANN）, Deep Belief Network（DBN）, Deep Auto-Encoder（DAE） and so on.

Key words: fault diagnosis, deep learning, improved wavelet auto-encoder, rolling bearing