An improved federated learning method based on MF1-FedAvg and MSRANet for machinery fault diagnosis

Current fault detection methods for rolling bearings suffer from insufficient data, which limits the generalizability of the models. Typically, conventional approaches train models with a significant amount of labeled data to improve reliability. However, centralized training poses potential risks of data privacy leakage. To address this issue, we propose a federated learning-based fault diagnosis model. In this method, fault diagnosis models for different clients are collaboratively trained by multiple entities with distinct fault characteristics, eliminating the need for third-party aggregation and thereby reducing the risk of data leakage. Specifically, we design a multiscale residual neural network with the ability to perform direct feature extraction from fault data. This proposed network integrates attention units for various scales, emphasizing key features of bearing faults and enhancing the fault recognition capability of local models. Moreover, to address the inherent problem in traditional federated learning frameworks—disparities in client contributions, leading to suboptimal model quality and prolonged training times—this research introduces an innovative weighted strategy based on multiclass F1 scores. This strategy assigns higher weight to high-quality local clients, thereby enhancing both model quality and training speed. Experiments were conducted on two authentic bearing datasets, and the results demonstrate that the proposed method can achieve an average reduction of approximately 15 % in training iterations compared to the federated averaging algorithm, coupled with an average enhancement of approximately 5 % in fault diagnosis accuracy. The experimental results indicate that the proposed method exhibits outstanding accuracy and robustness.