Bearing Fault Detection and Diagnosis Based on Federated Learning
Firstly,by studying the theoretical basis of bearing fault,the traditional vibration signal analysis method is introduced,and the defects of bearing fault algorithm are summarized.On this basis,convolutional neural network(CNN)is studied for model training and federated learning.Then,the FedAvg algorithm is used to aggregate the client models received by the server to obtain the global model parameters for bearing fault detection.Finally,it is verified that the bearing fault detection based on Federated learning is realized.A large number of experiments show that the algorithm is accurate and effective.
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