Chinese Journal of Ship Research, Volume. 20, Issue 4, 213(2025)

Deep residual shrinkage adaptive network-based cloud-edge-end collaborative fault diagnosis method for propulsion shafting system

Kaiwei YU1, Zirui LI2, Chong CHEN3, Jun WU1, and Haiping ZHU2
Author Affiliations
  • 1School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 2School of Mechanical Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 3China Ship Development and Design Center, Wuhan 430064, China
  • show less

    Objectives

    Aiming at problems including the fact that the fault diagnosis model of propulsion shafting systems under variable working conditions has poor generalization and cannot learn autonomously, and that the performance of the model is relatively fixed and cannot be updated online after it is deployed to the edge, this paper proposes a cloud-edge-end collaborative fault diagnosis method based on a deep residual shrinkage adaptive network.

    Methods

    First, the historical data of known operating conditions is collected and a deep residual shrinkage adaptive network model is built in the cloud through which reinforcement learning algorithms are introduced. These give the model the ability to update adaptively and learn data online under changing working conditions, thereby realizing online updating and adaptive performance enhancement. Model deployment and updating at the edge end are then realized by model slice distribution and edge slice aggregation, and real-time fault diagnosis is performed at the edge. Finally, the effectiveness of the proposed method is verified using a ship propulsion shaft system experimental bench.

    Results

    The results show that the proposed method is able to realize the online updating of the model under variable operating conditions, and the updated model has higher fault diagnosis accuracy compared with a non-updated model.

    Conclusion

    The results of this study can provide useful references for the fault diagnosis of propulsion shaft systems under variable operating conditions.

    Keywords
    Tools

    Get Citation

    Copy Citation Text

    Kaiwei YU, Zirui LI, Chong CHEN, Jun WU, Haiping ZHU. Deep residual shrinkage adaptive network-based cloud-edge-end collaborative fault diagnosis method for propulsion shafting system[J]. Chinese Journal of Ship Research, 2025, 20(4): 213

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Marine Machinery, Electrical Equipment and Automation

    Received: Feb. 4, 2024

    Accepted: --

    Published Online: Sep. 11, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.03779

    Topics