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

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.

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.

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.

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