The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Universal Domain Adaptation in Fault Diagnostics With Hybrid Weighted Deep Adversarial Learning
Abstract
In the past years, the practical cross-domain machinery fault diagnosis problems have been attracting growing attention, where the training and testing data are collected from different operating conditions. The recent advances in closed-set domain adaptation have well addressed the basic problem where the fault mode sets are identical in the source and target domains. While some attempts have also been made on the partial and open-set domain adaptations, no prior information of the target-domain fault modes can be usually available in the real industries, that forms a challenging problem in transfer learning. This article proposes a universal domain adaptation method for fault diagnosis, where no explicit assumption is made on the target label set. A hybrid approach with source class-wise and target instance-wise weighting mechanism is proposed for selective adaptation. By using additional outlier identifier, the proposed method can automatically recognize the unknown fault modes while achieving class-level alignments for the shared health states, without knowing the target label set. Experiments on two rotating machine datasets validate the proposed method, which is promising for practical applications under strong data uncertainties.
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Cites
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Adversarial Discriminative Domain Adaptation
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Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997 · cited 1×
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Cited by
Deep discriminative transfer learning network for cross-machine fault diagnosis
Qian, Q., Qin, Y., Luo, J., Wang, Y., & Wu, F. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109884 · Cited by 394
Lomazzi, L., Giglio, M., & Cadini, F. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106003 · Cited by 43
Metadata
- Authors
- Zhang, W., Li, X., Ma, H., Luo, Z., & Li, X.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2021-12
- Volume
- 17
- Issue
- 12
- Pages
- 7957-7967
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W3134162419
- Cited by
- 268
- References
- 32
- FWCI
- 23.21
- Type
- journal article
- Language
- en
- Open access
- closed
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