期刊
IEEE ACCESS
卷 7, 期 -, 页码 91216-91224出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2926234
关键词
Rolling bearing; fault diagnosis; deep transfer nonnegativity-constraint sparse autoencoder; parameter transfer learning; few labeled data
资金
- Major Research Plan of the National Natural Science Foundation of China [91860124]
- National Natural Science Foundation of China [51875459]
- Aeronautical Science Foundation of China [20170253003]
- Synergy Innovation Foundation of the University and Enterprise for Graduate Students in Northwestern Polytechnical University [XQ201901]
Rolling bearing fault diagnosis can greatly improve the safety of rotating machinery. In some cases, plenty of labeled data are unavailable, which may lead to low diagnosis accuracy. To deal with this problem, a deep transfer nonnegativity-constraint sparse autoencoder (DTNSAE) is proposed, which takes advantage of deep learning and transfer learning. First, a novel NSAE is adopted to enhance sparsity. Then, a base deep NSAE (DNSAE) is established to automatically capture the latent features from raw vibration signals. Next, a parameter transfer learning strategy is used to build the DTNSAE to tackle the diagnosis problems with a few labeled data. Finally, two datasets from different domains are used to verify the effectiveness of the proposed method. The testing results suggest that the proposed method is able to remove manual feature extraction and is more effective than the existing intelligent methods when only a few labeled data are available.
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