Fault Diagnosis of CNC Machine Tool Electric Spindle with WPD-TSNE-SVM Model
In order to improve the fault diagnosis efficiency of motorized spindle of NC machine tool,a WPD-TSNE-SVM combined model was designed.The main shaft vibration signal is decomposed by using the wavelet packet method,and the dimensionality reduction process of sample set TSNE is completed,and the fault classification of reconstructed features is completed via SVM.The mixed feature space vector of NC machine tool spindle signal was constructed,and the fault diagnosis was analyzed.The results show that the training sample data of TSNE method form regular distribution characteristics,and nonlinear SVM multi-fault classifier is used to achieve the accurate fault classification of wavelet packet mixed features.The nonlinear SVM diagnosis method based on the radial basis kernel function can achieve the higher accuracy.This method can diagnose the running faults of bearings,obtain higher maintenance efficiency,and ensure the running stability of CNC machine tool spindle.
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