By analyzing the formation mechanism of spindle rotation errors,regression forecasting model of spindle rotation errors which based on multi-sensor signal was built.Firstly,the LMD method and Pearson correlation coefficient method were used to extract feature values and optimize dimensionality reduction for the vibration signals,current signals,and acoustic emission signals of the front bearing,which solve the problem that types of original signals used in prediction of spindle rotation error of CNC machine tools were too single.Secondly,aiming at the nonlinear problems between various monitoring signals and rotation errors of the machine tool spindle,the RBF kernel function was used to achieve nonlinear prediction under multiple inputs and find complex relationships between datas.However,to establish RBF kernel function,the effective determination of width coefficient σ,penalty factor C and insensitive loss coefficient ε was a challenge in the model.Therefore,a support vector machine model based on particle swarm optimization algorithm was established to predict the spindle rotation errors.Once again,to evaluate the effectiveness of the model,a regression prediction model evaluation method for spindle rotation error based on mean square error,mean absolute error and coefficient of determination was proposed.Finally,experimental research was conducted on above prediction model in the i5m4 CNC machining center.The results showed that the mean square error of PSO-SVM regression prediction model was 0.19％,the average absolute error was 4.58％,the coefficient of determination was 0.923 7.Compared with the model before optimization,the PSO-SVM regression forecasting model can predict the spindle rotation errors accurately and effectively.
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