期刊
IEEE SENSORS JOURNAL
卷 20, 期 15, 页码 8413-8422出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2020.2975286
关键词
Feature extraction; Fault diagnosis; Convolution; Training; Fault detection; Kernel; Convolutional neural networks; Deep adversarial transfer learning; fault diagnosis; convolutional neural network; emerging fault detection
资金
- National Key Research and Development Program of China [2018YFB1702400]
- National Natural Science Foundation of China [51875208, 51705156]
Deep transfer learning has attracted many attentions in machine intelligent fault diagnosis. However, most existed deep transfer learning algorithms encounter difficulties to detect a new emerging fault in target domain because these methods assume that the source and target domains have the same fault categories. Unfortunately, in real-world applications, new fault may emerge during machine running, which is not the same as those faults for training diagnosis models. To solve this problem, a novel fault diagnosis method named deep adversarial transfer learning network (DATLN) is proposed for new emerging fault detection. First, a one-dimension convolutional neural network is constructed to learn invariant features from the raw vibration signals of the source and target domains. Then, a multiple label classifier is trained to recognize known fault classes of the source and target domains. Finally, a decision boundary is built for the new emerging fault detection by training a classifier to recognize some target samples as new ones. Experiments on rolling bearing and gearbox demonstrate that the DATLN can implement the faults recognition with high accuracy and outperform other transfer learning methods when a new fault emerging in the target domain.
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