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Optimal transport strategy-based meta-attention network for fault diagnosis of rotating machinery with zero sample

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Abstract

Deep learning-based methods are widely applied to fault diagnostics, which depend on adequate samples. However, fault samples are often limited and even not available in industrial applications. To solve this problem, an optimal transport strategy-based meta-attention network (OTS-MAN) is proposed for the fault diagnosis by exploiting the fault knowledge learned from few source domains to diagnose the target domain with zero sample. Firstly, a new meta-attention network is built to mine discriminative features of each class from the source domain. Then, an optimal transport strategy is designed to align the feature distribution of each category between known fault in the source domain and unknown fault in the target domain. Finally, the similarity scores are obtained to assess the health status of the target domain. The proposed OTS-MAN is trained only with known source domain data and can diagnose unknown faults without previous access to target domain data. The validity of the proposed method is implemented through using two cases. The results indicate that the OTS-MAN has a better fault diagnosis accuracy than existing methods, and its noise immunity is also improved.

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Data availability

The data analyzed in this study is partly from https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/ and partly available on request from the corresponding author upon reasonable request.

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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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Authors and Affiliations

  1. School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Ke Wu, Kaiwei Yu, Jun Wu & Yan Liu

  2. China Ship Development and Design Center, Wuhan, 430060, China

    Chong Chen

  3. National Center of Technology Innovation for Intelligent Design and Numerical Control, Wuhan, 430074, China

    Jun Wu

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  1. Ke Wu
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  4. Jun Wu
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Contributions

Ke Wu: Writing-Original draft preparation, Methodology, Formal analysis. Kaiwei Yu: Software, Validation, Visualization, Investigation. Chong Chen: Project administration. Jun Wu: Writing-Reviewing and Editing, Conceptualization, Methodology, Supervision, Funding acquisition. Yan Liu: Conceptualization, Methodology.

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Correspondence to Jun Wu.

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Wu, K., Yu, K., Chen, C. et al. Optimal transport strategy-based meta-attention network for fault diagnosis of rotating machinery with zero sample. Appl Intell 54, 6799–6815 (2024). https://doi.org/10.1007/s10489-024-05524-9

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