Journal
MEASUREMENT
Volume 151, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2019.107227
Keywords
Feature-transfer learning; Instance-transfer learning; Long-short term memory recurrent neural network; Joint distribution adaptation; Grey wolf optimization algorithm
Funding
- National Natural Science Foundation of China [51875459]
- major research plan of the National Natural Science Foundation of China [91860124]
- Aeronautical Science Foundation of China [20170253003]
- Research Funds for Interdisciplinary subject, NWPU
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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. (C) 2019 Elsevier Ltd. All rights reserved.
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