Abstract
Transfer learning methods have received abundant attention and extensively utilized in cross-domain fault diagnosis, which suppose that the label sets in the source and target domains are coincident. However, the open set domain adaptation problem which include new fault modes in the target domain is not well solved. To address the problem, an unknown-class recognition adversarial network (UCRAN) is proposed for the cross-domain fault diagnosis. Specifically, a three-dimensional discriminator is designed to conduct domain-invariant learning on the source domain, target known domain and target unknown domain. Then, an entropy minimization is introduced to determine the decision boundaries. Finally, a posteriori inference method is developed to calculate the open set recognition weight, which are used to adaptively weigh the importance between known class and unknown class. The effectiveness and practicability of the proposed UCRAN is validated by a series of experiments. The experimental results show that compared to other existing methods, the proposed UCRAN realizes better diagnosis performance in different domain transfer task.
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Data availability
The data analyzed in this study is partly from https://mb.uni-paderborn.de/kat/forschung/datacenter/bearing-datacenter/ and partly available on request from the corresponding author upon reasonable request.
Abbreviations
- UCRAN:
-
Unknown-class recognition adversarial network
- DA:
-
Domain adaptation
- CSDA:
-
Closed set domain adaptation
- OSDA:
-
Open set domain adaptation
- SD:
-
Source domain
- TDK:
-
Target domain-known
- TDU:
-
Target domain-unknown
- \({D}_{s}\) :
-
Source domain datasets
- \({D}_{t}\) :
-
Target domain datasets
- \({\mathcal{C}}_{s}\) :
-
The set of source domain labels
- \({\mathcal{C}}_{t}\) :
-
The set of target domain labels
- \(\mathcal{C}\) :
-
The known labels
- \(\overline{{\mathcal{C}}}_{t}\) :
-
The target-unknown labels
- \({f}_{{\theta }_{G}}\) :
-
Feature extraction module
- \({f}_{{\theta }_{D}}\) :
-
Domain discriminator
- \({f}_{{\theta }_{E}}\) :
-
Unknown recognizer
- \({f}_{{\theta }_{C}}\) :
-
Domain classifier
- \({WT}_{f}\) :
-
Operation of Continuous wavelet transform
- \({L}_{ce}\) :
-
Cross-entropy loss function
- \({D}_{SD}\) :
-
Domain discriminator for source domain
- \({D}_{TDK}\) :
-
Domain discriminator for target known domain
- \({D}_{TDU}\) :
-
Domain discriminator for target unknown domain
- \({L}_{D}^{s}\) :
-
The source domain discrimination loss function
- \({L}_{D}^{t}\) :
-
The target domain discrimination loss function
- \({w}_{x}\) :
-
Open set recognition weight
- \({L}_{D}^{TDK}\) :
-
The target domain-known domain discrimination loss function
- \({L}_{D}^{TDU}\) :
-
The target domain-unknown domain discrimination loss function
- \({L}_{D}\) :
-
Overall domain discrimination loss of
- \({L}_{G}\) :
-
Overall generator loss
- \({L}_{e}^{s}\) :
-
The loss function of source domain
- \({L}_{ent}^{t}\) :
-
The entropy loss
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Acknowledgements
This research is supported in part by the National Natural Science Foundation of China under the Grant No. 51875225, and in part by Hubei Provincial Natural Science Foundation for Innovation Groups under Grant No. 2021CFA026.
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Ke Wu: Writing-Original draft preparation, Methodology, Formal analysis. Wei Xu: Software, Validation, Visualization, Investigation. Qiming Shu: Project administration. Wenjun Zhang: Conceptualization, Methodology. Xiaolong Cui: Editing, Supervision. Jun Wu: Writing-Reviewing and Editing, Conceptualization, Methodology, Supervision, Funding acquisition.
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Wu, K., Xu, W., Shu, Q. et al. Unknown-class recognition adversarial network for open set domain adaptation fault diagnosis of rotating machinery. J Intell Manuf (2024). https://doi.org/10.1007/s10845-024-02395-2
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DOI: https://doi.org/10.1007/s10845-024-02395-2