| Citation: | ZHANG Zhenliang, LIU Junqiang, HUANG Liang, et al. A bearing fault diagnosis method based on semi-supervised and transfer learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2291-2300. doi: 10.13700/j.bh.1001-5965.2019.0082(in Chinese)
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Aimed at the problems of insufficient prediction accuracy and over-fitting in the fault diagnosis process of aero-engine bearing, a semi-supervised integrated learning device based on transfer learning (SSIT) is proposed to predict engine bearing fault. First, transfer learning based improved extreme learning machine (TELM) and support vector machines (TSVM) were trained by adding the high-similarity sample of different target space to the original sample space, which is integrated to identify the tag sample with the corresponding learning. Then integrate the same cluster learner with the corresponding base learner to identify the unlabeled samples, Next, the constituted semi-supervised learning device constantly adjusts the initial learning unit weight, and continues to integrate semi-supervised learning recognition results into SSIT, which will be used to identify faults after feature identification and extraction by this learning machine. The experimental results clearly show that this algorithm can effectively reduce the negative transfer effect in transfer learning, improve the transfer accuracy by about 10%, reduce the over-fitting effect in machine learning, and improve the stability of semi-supervised learning. Compared with the existing prediction method, this algorithm can improve the accuracy by more than 9%.
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