Journal
MEASUREMENT
Volume 205, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2022.112139
Keywords
Lightweight and robust; Feature fusion; Adversarial learning; Channel residual
Funding
- China National Innovation and Development Project of Industrial Internet [TC190H3WR]
- National Natural Science Foundation of China [52272440, 51875375]
- China Postdoctoral Science Foundation [2021M701503]
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This article proposes a lightweight and robust model for cross-domain bearing fault diagnosis. By using feature fusion-based unsupervised adversarial learning, the model addresses the weaknesses of large size, complex calculation, and weak anti-noise ability. Experimental results demonstrate that the proposed model outperforms existing methods in terms of size, computation, and robustness.
Cross-domain bearing fault diagnosis models have weaknesses such as large size, complex calculation and weak anti-noise ability. Hence, a lightweight and robust model via feature fusion-based unsupervised adversarial learning (LRFFUAL) is proposed, which could be a special benefit for practical engineering applications. A main innovation lies in a customized feature fusion block to achieve a tradeoff between model lightweight and robustness. Accordingly, a channel residual strategy is proposed to apply residual techniques for channels with weak feature information to achieve data augmentation. Concerning cross-domain tasks with huge distribution discrepancy, a new adversarial learning strategy is proposed to improve model convergence rate by inputting marginal features into a discriminator. Experimental results show that the proposed LRFFUAL has advantages of smaller size, less computation, and stronger robustness compared with other existing methods.
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