期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 67, 期 11, 页码 9747-9757出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2019.2953010
关键词
Kernel; Computational modeling; Task analysis; Measurement; Adaptation models; Fault diagnosis; Data models; Deep transfer learning; domain adaptation; intelligent fault diagnosis (IFD); maximum mean discrepancy
资金
- National Key R&D Program of China [2018YFB1306100]
- National Natural Science Foundation of China (NSFC)-Zhejiang Joint Fund for the Integration of Industrialization and Informatization [U1709208]
- National Natural Science Foundation of China [61673311]
Deep transfer-learning-based diagnosis models are promising to apply diagnosis knowledge across related machines, but from which the collected data follow different distribution. To reduce the distribution discrepancy, Gaussian kernel induced maximum mean discrepancy (GK-MMD) is a widely used distance metric to impose constraints on the training of diagnosis models. However, the models using GK-MMD have three weaknesses: 1) GK-MMD may not accurately estimate distribution discrepancy because it ignores the high-order moment distances of data; 2) the time complexity of GK-MMD is high to require much computation cost; 3) the transfer performance of GK-MMD-based diagnosis models is sensitive to the selected kernel parameters. In order to overcome the weaknesses, a distance metric named polynomial kernel induced MMD (PK-MMD) is proposed in this article. Combined with PK-MMD, a diagnosis model is constructed to reuse diagnosis knowledge from one machine to the other. The proposed methods are verified by two transfer learning cases, in which the health states of locomotive bearings are identified with the help of data respectively from motor bearings and gearbox bearings in laboratories. The results show that PK-MMD enables to improve the inefficient computation of GK-MMD, and the PK-MMD-based diagnosis model presents better transfer results than other methods.
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