The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Novel Joint Transfer Network for Unsupervised Bearing Fault Diagnosis From Simulation Domain to Experimental Domain
Abstract
Unsupervised cross-domain fault diagnosis of bearings has practical significance; however, the existing studies still face some problems. For example, transfer diagnosis scenarios are limited to the experimental domain, cross-domain marginal distribution and conditional distribution are difficult to align simultaneously, and each source-domain sample is assigned with equal importance during the domain adaptation process. Aiming at the above-mentioned challenges, this article proposes a novel joint transfer network for unsupervised bearing fault diagnosis from the simulation domain to the experimental domain. The sufficient bearing simulation data containing rich fault label information are used to construct the source domain to reduce the dependence on the resources of laboratory test rigs. An improved loss function embedded with joint maximum mean discrepancy is designed to achieve simultaneous alignments of marginal and conditional distributions across domains in unsupervised scenarios. A weight allocation mechanism for each source-domain sample is developed to suppress negative transfer. Two experimental datasets collected from laboratory test rigs are used as the target domains to validate the effectiveness of the proposed method. The results show that the proposed method is superior to other popular unsupervised cross-domain fault diagnosis methods.
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Cites
Domain-Adversarial Training of Neural Networks
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Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks
Xia, M., Li, T., Xu, L., Liu, L., & de Silva, C. W. (2018) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2017.2728371 · Cited by 723 · cited 1×
“In recent years, thanks to the rapid development of artificial intelligence technology, deep learning models have been developed and successfully applied in the field of end-to-end machinery intelligent fault diagnosis, which have received extensive attention from scholars worldwide [9]–[16].”
BackgroundHan, T., Liu, C., Yang, W., & Jiang, D. (2020) · ISA Transactions · doi:10.1016/j.isatra.2019.08.012 · Cited by 578
Zhao, Z., Zhang, Q., Yu, X., Sun, C., Wang, S., Yan, R., & Chen, X. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3116309 · Cited by 526
Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network
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Self-citationVibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery
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Cited by
Zhao, K., Jia, Z., Jia, F., & Shao, H. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105860 · Cited by 234
Self-citationYan, S., Shao, H., Xiao, Y., Liu, B., & Wan, J. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102441 · Cited by 201
Self-citationZhao, K., Hu, J., Shao, H., & Hu, J. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109246 · Cited by 149
Self-citationPei, H., Si, X., Li, T., Zhang, Z., & Lei, Y. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3310482 · Cited by 33
Metadata
- Authors
- Xiao, Y., Shao, H., Han, S., Huo, Z., & Wan, J.
- Venue
- IEEE/ASME Transactions on Mechatronics
- Published
- 2022-12
- Volume
- 27
- Issue
- 6
- Pages
- 5254-5263
- ISSN
- 1083-4435 (ISSN-L)
- 1941-014X
- OpenAlex
- W4285245107
- Cited by
- 334
- References
- 39
- FWCI
- 30.47
- Type
- journal article
- Language
- en
- Open access
- closed
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- Xiao, Y., Shao, H., Han, S., Huo, Z., & Wan, J. (2022). Novel Joint Transfer Network for Unsupervised Bearing Fault Diagnosis From Simulation Domain to Experimental Domain. IEEE/ASME Transactions on Mechatronics. https://doi.org/10.1109/tmech.2022.3177174
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