期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 64, 期 3, 页码 2296-2305出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2016.2627020
关键词
Deep neural network (DNN); domain adaptation; fault diagnosis
资金
- Natural Science Foundation of Guangdong [2015A030313881]
- Research Foundation of Shenzhen [JCYJ20140509172959962]
- National Natural Science Foundation of China [61673239]
- Shenzhen Key Laboratory of Space Robotic Technology and Telescience
In recent years, machine learning techniques have been widely used to solve many problems for fault diagnosis. However, in many real-world fault diagnosis applications, the distribution of the source domain data (on which the model is trained) is different from the distribution of the target domain data (where the learned model is actually deployed), which leads to performance degradation. In this paper, we introduce domain adaptation, which can find the solution to this problem by adapting the classifier or the regression model trained in a source domain for use in a different but related target domain. In particular, we proposed a novel deep neural network model with domain adaptation for fault diagnosis. Two main contributions are concluded by comparing to the previous works: first, the proposed model can utilize domain adaptation meanwhile strengthening the representative information of the original data, so that a high classification accuracy in the target domain can be achieved, and second, we proposed several strategies to explore the optimal hyperparameters of the model. Experimental results, on several real-world datasets, demonstrate the effectiveness and the reliability of both the proposed model and the exploring strategies for the parameters.
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