An adaptive deep transfer learning method for bearing fault diagnosis. (February 2020)

• Record Type: Journal Article
• Authors: Wu, Zhenghong; Jiang, Hongkai; Zhao, Ke; Li, Xingqiu
• Is Part Of: Measurement. Volume 151(2020)
• Publication Date: 2020-02
• Subjects: Feature-transfer learning -- Instance-transfer learning -- Long-short term memory recurrent neural network -- Joint distribution adaptation -- Grey wolf optimization algorithm; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.107227
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Constructing a LSTM model to generate some auxiliary datasets. Applying JDA to reduce the differences in probability distributions of two datasets. GWO algorithm is introduced to adaptively learn JDA key parameters. Abstract: Bearing fault diagnosis has made some achievements based on massive labeled fault data. In practical engineering, machines are mostly in healthy and faults seldom happen, it's difficult or expensive to collect massive labeled fault data. To solve the problem, an adaptive deep transfer learning method for bearing fault diagnosis is proposed in this paper. Firstly, a long-short term memory recurrent neural network model based on instance-transfer learning is constructed to generate some auxiliary datasets. Secondly, joint distribution adaptation, a feature-transfer learning method, which is used to reduce the differences in probability distributions between an auxiliary dataset and target domain dataset. Finally, grey wolf optimization algorithm is introduced to adaptively learn key parameters of joint distribution adaptation. The proposed method is verified with two kinds of datasets, and the results demonstrate the effectiveness and robustness of the proposed method when the labeled fault data are scarce.