多源传感器数据下基于注意力机制与长短期记忆网络的轴承故障诊断与寿命预测OA北大核心CSTPCD

To solve the problem of the poor diagnosis effect of rolling bearing faults in noisy environ-ments,we propose a new bearing fault diagnosis method based on an attention mechanism and a long short-term memory(LSTM)network using multisource sensor data.First,we normalize the one-dimensional(1D)data collected by multisource sensors and then construct a double-channel twin convolutional network with adaptive batch normalization technology to extract effective features and perform data fusion.Second,the fusion data are input into the improved 1D coordinate atten-tion(CoordAtt)which is capable of considering the relationship between channels and position in-formation simultaneously.Third,we use the LSTM layer to extract time features and evaluate the diagnosis effect by the loss function after label smoothing regularization.We use the new optimizer Adan for optimization.Finally,the diagnosis model is applied to a test set,and the diagnosis re-sults of fault categories are output.We compare the diagnostic accuracy of the model under differ-ent test set ratios,determine that the optimal ratio is 0.3,and test it in a noisy environment.The experimental results show that the proposed method can better resist the influence of noisy environ-ments.The validity of the CoordAtt-LSTM model in life prediction is verified by experimental re-sults on the C-MAPSS dataset.