MA X N, LI H. Bearing fault diagnosis method based on dynamic clustering federated learning [J]. Journal of Henan Polytechnic University (Natural Science), 2025, 44(5): 1-8.

DOI: 10.16186/j.cnki.1673-9787.2024080007

Received: 2024/08/08

Revised: 2024/09/23

Published:2025/07/23

Bearing fault diagnosis method based on dynamic clustering federated learning

Ma Xinna, Li Hao

School of Information Science and Technology， Shijiazhuang Tiedao University， Shijiazhuang 050043， Hebei， China

Abstract: Objectives In industrial scenarios， bearing fault data held by clients are typically non-independent and identically distributed （Non-IID）， which adversely affects the performance of federated learning （FL） systems. This study aimed to address the performance degradation of FL under such conditions. Methods This paper proposed a bearing fault diagnosis method based on dynamic clustering federated learning （DCFed）. The method grouped clients with similar data distributions into clusters to mitigate model divergence caused by data heterogeneity. It comprised three main stages： parameter selection， clustering， and aggregation. In the parameter selection stage， optimal model parameters were provided to the clients. During clustering， K-means clustering was performed based on cosine similarity calculated from client inference results within the current training round， encouraging clients within each cluster to approximate an independent and identically distributed （IID） pattern. In the aggregation stage， local models were first averaged within clusters and then aggregated across clusters to form an efficient and personalized global model. Results The proposed DCFed method was evaluated on the Case Western Reserve University （CWRU） bearing dataset. Under the Non-IID scenario， the global model achieved an accuracy of 95.23%， which was close to that of centralized training methods. DCFed converged within just 150 training rounds， demonstrating faster convergence and higher accuracy compared to other methods such as FedProx and Fedora. Further experiments examining key parameters and clustering methods confirmed the robustness and stability of the proposed approach. Conclusions Under Non-IID conditions， the proposed method effectively mitigated the impact of data heterogeneity on federated learning and accelerates convergence. Experimental results demonstrated that the method performed excellently in bearing fault diagnosis tasks.

Key words: data heterogeneity; clustering federated learning; fault diagnosis; deep learning; data privacy