基于电流的主轴性能退化评估方法

为提高精密机床主轴检测的准确性，提出了一种基于电流的主轴性能退化评估方法.建立了主轴性能退化模型，使主轴状态便于监测和评估.首先采用小波包阈值对电流信号进行去噪处理，对去噪后的电流信号提取时频域特征量，构造多域特征空间.然后利用主成分分析法（PCA）进行数据降维，用降维后的样本进行支持向量机回归建模.采用粒子群算法（PSO）对支持向量机模型进行参数优化，以获得最优性能退化模型.将该模型应用于主轴实验台主轴性能退化评估，实验结果表明该方法原理正确，可以准确评价主轴性能. 

Evaluation Method of Spindle Performance Degradation Based on Current

In order to improve the accuracy of spindle detection for precision machine tools, a method was proposed based on current for evaluating the performance degradation of spindle. A performance degradation model of the spindle was established to facilitate the monitoring and evaluation of the spindle condition. Firstly, the wavelet packet threshold was used to denoise the current signal, and then the multi domain feature space was constructed by extracting the time-frequency features of the denoised current signal. Then the principal component analysis (PCA) was used for data dimensionality reduction, and the dimensionality reduction of samples was used for support vector machine regression modeling. the particle swarm optimization (PSO) algorithm was used to optimize the parameters of the support vector machines (SVM) model to obtain the optimal performance degradation model. Finally, the model was applied to the evaluation of spindle performance degradation in a experiment platform. The experimental results show that the method is correct and can accurately evaluate spindle performance.