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A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions

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Abstract

Purpose

Due to the influence of external factors such as noise, the operating conditions of rotating machinery in actual operation are not constant, and it is difficult to obtain high diagnostic accuracy using differently distributed training data and test data for fault diagnosis.

Methods

To solve the problem of fault diagnosis under variable working conditions, we come up with a fault diagnosis approach on the account of Inception V1 module with self-attention mechanism for domain adversarial transfer network (IS-DATN). First, a feature extractor based on the Inception V1 module and the self-attention mechanism is constructed to learn domain-invariant features from the training data of the source and target domains; then, the feature extractor and the domain discriminator are trained using an adversarial training strategy to optimize the performance of both, and the labeled classifier is trained to enable accurate fault identification; finally, the test data are fed into the model, and the labeled classifier can accurately assign the unlabeled target domain data to each category, which enables fault diagnosis under variable running requirements.

Results and Conclusion

The experiments in this article use the Case Western Reserve University and Laboratory self-test rolling bearing dataset and show that the presented approach can reduce the domain distribution differences, and obtain 95.43% diagnostic accuracy in case of a large difference in working conditions and 100% diagnostic accuracy in case of similar working conditions, compared with other methods, the proposed method has better transfer effect and higher diagnostic accuracy.

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Acknowledgements

This work was financially supported by The Key Program of Natural Science Foundation of Tianjin (21JCZDJC00770), and The National Natural Science Foundation of China and the Civil Aviation Administration of China joint funded projects (U1733108).

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Authors and Affiliations

  1. School of Mechanical Engineering, Tiangong University, Tianjin, 300387, China

    Zhiwu Shang, Jie Zhang, Wanxiang Li, Shiqi Qian & Maosheng Gao

  2. Tianjin Key Laboratory of Modern Mechanical and Electrical Equipment Technology, Tiangong University, Tianjin, 300387, China

    Zhiwu Shang, Jie Zhang, Wanxiang Li, Shiqi Qian & Maosheng Gao

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  1. Zhiwu Shang
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  2. Jie Zhang
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Correspondence to Zhiwu Shang.

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Shang, Z., Zhang, J., Li, W. et al. A Domain Adversarial Transfer Model with Inception and Attention Network for Rolling Bearing Fault Diagnosis Under Variable Operating Conditions. J. Vib. Eng. Technol. 12, 1–17 (2024). https://doi.org/10.1007/s42417-022-00823-2

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  • DOI: https://doi.org/10.1007/s42417-022-00823-2

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