Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (5): 263-269.DOI: 10.3778/j.issn.1002-8331.1811-0372

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Method of Improved Deep Wavelet Auto-Encoder in Bearing Fault Diagnosis

DU Xiaolei, CHEN Zhigang, XU Xu, ZHANG Nan   

  1. 1.College of Electrical and Mechanical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
    2.Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
  • Online:2020-03-01 Published:2020-03-06



  1. 1.北京建筑大学 机电与车辆工程学院,北京 100044
    2.北京建筑大学 北京市建筑安全监测工程技术研究中心,北京 100044


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



关键词: 故障诊断, 深度学习, 改进小波自编码器, 滚动轴承