Abstract
In engineering, the fault data unevenly distribute and difficultly share, which causes that the existing fault diagnosis methods cannot recognize the newly added fault types. An intelligent diagnosis method for machine fault is proposed based on federated learning. Firstly, the local fault diagnosis models diagnosing the existing fault data and the newly added fault data are established by deep convolutional neural network. Then, the weight parameters of local models are fused into global model parameters by federated learning. Finally, the global model parameters are transmitted to each local model. Therefore, each local model update into a global shared model which can recognize the newly added fault types. The proposed method is verified by bearing data. Compared with the traditional model, which can only diagnose the existing fault data but cannot recognize newly added fault types, the federated fault diagnosis model fusing weight parameters can diagnose newly added faults without exchanging the data, and the accuracy is 100%. The proposed method provides an effective method to solve the poor sharing of fault data and poor generalization of fault diagnosis model for mechanical equipment.
Highlights
An intelligent diagnosis model for mechanical fault based on federated learning was proposed and verified by two bearing cases
The federated fault diagnosis model established based on the proposed method can recognize the newly added fault type, because the weight parameters of local model are fused and updated during the training
The proposed model achieves the effect of data sharing by fusing the models with different fault recognition capabilities to recognize the different fault types
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. [14], deep transfer learning was applied to diagnose the fault of rolling bearing under variable conditions, and the recognition rate is improved by 2–8%. [15], a deep convolutional neural network was used to recognize the fault types based on the fusion data collected by the horizontal and vertical sensors mounted in the gearbox. [15], a deep convolutional neural network used to recognize the fault types based on the fusion data collected by the horizontal (1) The proposed method can effectively recognize new fault types and improve the vertical sensors mounted the gearbox. A local fault diagnosis model is established for different types of fault is a tran fused into a federated fault diagnosis model by the proposed method, which solves model of fault diagnosis was established by deep residual neural network.
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