Fault diagnosis method for different specification rolling bearings based on the method of federated model transfer learning

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (22) : 184-192.

KANG Shouqiang1,XIAO Yang1,WANG Yujing1,WANG Qingyan1,LIANG Xintao1,V.I.Mikulovich2

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Abstract

The issues of data privacy and security have gained attention gradually, for each user, the private vibration data of rolling bearings existing the problems of isolated data island and the non-sharing of data among different users. Meanwhile, the distributions of vibration data for different specification rolling bearings have great difference, and some labeled data is scarce. So, above reasons lead to low diagnostic accuracy. For the above problems, a fault diagnosis frame of different specifications rolling bearings based on federated model transfer learning is proposed. The vibration data of multiple users are processed using short-time Fourier transform, and then time-frequency spectrum datasets can be constructed. Each user trains the local model and uploads model parameters to the server, the local model parameters transfer strategy of federated learning can be improved by introducing difference update and parameter thinning algorithm. By using the federated averaging algorithm, the server aggregates local model parameters and updates the local model, then after iterating, a shared model for transfer learning can be obtained. The server uses layer-by-layer unfreeze strategy to reserve part of the shared model parameters and send them to each user, and users fine-tune the shared model using local data, then getting a personalized model that works for each user. Through the experimental verification, the proposed method can realize fault diagnosis of rolling bearings for different specifications in the case of isolated data island and lack of labels, it also has high accuracy and good generalization performance.

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federated learning / transfer learning / rolling bearing / fault diagnosis / different specifications

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KANG Shouqiang1,XIAO Yang1,WANG Yujing1,WANG Qingyan1,LIANG Xintao1,V.I.Mikulovich2. Fault diagnosis method for different specification rolling bearings based on the method of federated model transfer learning[J]. Journal of Vibration and Shock, 2023, 42(22): 184-192

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