Tool Condition Monitoring Based on CEEMDAN and BiLSTM-SN-ECA
Aiming at the difficulties in extracting tool degradation features and the many parameters of tra-ditional spatiotemporal network models,a tool wear monitoring model based on adaptive noise-complete empirical mode decomposition(CEEMDAN)and improved lightweight spatiotemporal network(BiLSTM-SN-ECA)is proposed.Firstly,the tool vibration signal is decomposed by CEEMADAN,and the modal component is combined with the vibration signal to construct a feature matrix.Secondly,ECA is used to im-prove the basic unit of ShuffleNetv2,and the overall structure of ShuffleNetv2 is optimized to construct the BiLSTM-SN-ECA network model.Finally,the feature matrix is input into the model for feature learning and wear prediction.The MAE and RMSE of the predicted values of the proposed method are 1.246 μm and 2.065 μm,and the results show that the proposed method can reduce the number of parameters and training time of the traditional spatiotemporal network model,and improve the prediction accuracy.
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