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A Semi-Supervised Federated Learning Fault Diagnosis Method Based on Adaptive Class Prototype Points for Data Suffered by High Missing Rate

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Abstract

With the development of Autonomous Marine Vehicles, research on real-time health monitoring based on data from remote monitoring and shore-based control center has attracted more and more attention. However, the feature extraction capability of each local client model is limited by the size and quality of data collected by remote monitoring. For clients with zero-shot, it is necessary to design an efficient federated learning framework which allows shore-based federation center with few-shot help to strengthen the feature extraction capability of clients. In this paper, a semi-supervised federated learning strategy based on adaptive learning mechanism of prototype points is proposed to solve the problem that clients with zero-shot have difficulty in establishing reliable fault diagnosis models when the quality of client data is inhomogeneous. By designing an adaptive prototypical network learning mechanism driven by sample quality and a dynamic in-and-out mechanism for local pseudo-labeled dataset, the few-shot learning capability of federation center as well as the reliability of local client’s prototype points are enhanced to alleviate the impact of unreliable pseudo-label on semi-supervised learning. The experiment validation on the rolling bearing dataset provided by Case Western Reserve University can come to an accuracy increasement of 13.12%, which indicates that the proposed Adpro-SSFL outperforms existed semi-supervised federated learning method when data suffered by high missing rate due to the interference of external factors on the signal acquisition sensor.

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Data Availability

The rolling bearing dataset is available for download from the Case Western Reserve University (CWRU) Bearing Data Center Website at https://engineering.case.edu/bearingdatacenter/welcome [51]. The authors would like to thank Case Western Reserve University for providing bearing vibration data.

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Acknowledgements

The authors would like to thank Case Western Reserve University for

providing bearing vibration data.

Funding

This work was supported by National Natural Science Foundation of China (Grant numbers 62073213 and 52,205,111).

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Authors and Affiliations

  1. School of Logistic Engineering, Shanghai Maritime University, Shanghai, China

    Funa Zhou, Wei Xu, Chaoge Wang, Xiong Hu & Tianzhen Wang

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  1. Funa Zhou
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Contributions

All authors contributed to the study conception and design. Supervision,

review and editing of manuscript were performed by Funa Zhou. The validation and original draft.

of the manuscript was performed by Wei Xu. And all authors commented on previous versions of.

the manuscript. All authors read and approved the final manuscript.

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Correspondence to Funa Zhou.

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Zhou, F., Xu, W., Wang, C. et al. A Semi-Supervised Federated Learning Fault Diagnosis Method Based on Adaptive Class Prototype Points for Data Suffered by High Missing Rate. J Intell Robot Syst 109, 93 (2023). https://doi.org/10.1007/s10846-023-02025-8

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