基于小波包与SVM的滚珠丝杠故障诊断

采取小波包方法对数控机床滚珠丝杠故障信号进行分析，结合时域分析，提取不同故障状态下的特征值，得到支持向量机（SVM）的输入特征向量；研究并确定了应用二叉树算法与RBF核函数，采用遗传算法对SVM的参数寻优，建立了SVM多故障分类器，实现了滚珠丝杠的故障诊断与分类。最后通过实验结果证明了多故障分类器的可行性与有效性。

Ball Screw Fault Diagnosis Based on Wavelet Packet and Support Vector Machine

Wavelet Packet method is used to analyse fault signals from the Ball Screw of CNC machine. By combing with the time-domain analysis, feature values under different fault conditions were drawn so as to achieving the feature vector which was put into the Support Vector Machine (SVM). The binary tree algorithm and RBF kernel function were studied and chosen, then the genetic algorithm (GA) for the optimization of SVM parameters was used, lastly the SVM multi-fault classifier was established to realize fault diagnose and classify of Ball Screw. Finally through experimental results, that shows the feasibility and validity of this multi-fault classifier.