基于深度残差收缩自适应网络的推进轴系云边端协同故障诊断方法

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

  • 摘要:
    目的 变工况下推进轴系故障诊断模型泛化性差、无法自主学习,模型部署到边缘设备后,性能相对固定,无法在线更新,针对上述问题,提出一种基于深度残差收缩自适应网络的云边端协同故障诊断方法。
    方法 首先,收集推进轴系运行过程中已知工况历史数据,在云端建立深度残差收缩自适应网络模型,通过引入强化学习算法,使模型具备自适应更新能力,在线学习变化工况下的数据,实现模型的在线更新与性能的自适应提升;然后,通过模型分片下发,边缘分片聚合方式,实现边缘端模型部署更新;最后,在边缘端进行实时故障诊断。基于船舶推进轴系实验台,验证所提出方法的有效性。
    结果 结果表明在变工况下所提方法能够实现模型在线更新,且更新后模型相比未更新模型有更高的故障诊断精度。
    结论 研究成果为变工况下推进轴系故障诊断提供参考。

     

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

     

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