A Data Privacy Protection Diagnosis Framework for Multiple Machines Vibration Signals Based on a Swarm Learning Algorithm

Bearing fault diagnosis is essential for monitoring rotating machinery and equipment operating conditions. They can also identify safety hazards and avoid economic losses promptly. However, there is a shortage of tagged fault data in most factories, and it is challenging to ask them to exchange labeled fault data, especially considering the data privacy needs of customers. This article proposes a swarm learning (SL) framework that combines adversarial domain networks with convolutional neural networks (CNNs) to address this problem. The framework regards every factory as an edge-computing node and solves labeled data insufficiency and privacy protection by fusing network parameters. First, a CNN is used to compute each node, and leaders are dynamically selected to merge model parameters during the training process. Second, an adversarial domain network minimizes the feature distribution variance between nodes. Finally, an SL algorithm was used to select virtual central nodes to determine the exchange process of the model parameters among different nodes. Four datasets were used to design the experiments and demonstrate the proposed approach's reliability. The experimental results show that the proposed framework can improve computational efficiency and reduce communication costs without relying on a central server. A final shared model can also achieve enhanced accuracy in fault diagnosis at each edge node.