基于遗传算法优化的支持向量机在齿轮故障声发射检测中的应用

Application of the Support Vector Machine based on Genetic Algorithm Optimization on the Acoustic Emission Detection of Gear Fault

支持向量机能够在训练样本较小的情况下,避免过学习现象,使泛化能力最大化。研究了支持向量机的参数无法自适应进行选取的问题,利用遗传算法具有全局搜索的性质,对支持向量机的参数选取进行最优化处理,得到支持向量机的最优参数。应用此法对齿轮故障声发射检测进行了研究,实验系统由PCI-2声发射系统和旋转机械振动故障模拟试验台组成,齿轮故障分类的精度比优化之前提高了10%,对齿轮故障诊断具有重要意义。

The support vector machine(SVM) can avoid the overlearning phenomenon in the case of small training samples,so that the generalization ability can be maximized. The problem that the SVM parameters cannot be selected adaptively is studied. By using the global search characteristic of the genetic algorithm,the parameters of the support vector machine are optimized and the optimal parameters of the support vector machine are obtained. This method is used to study the acoustic emission detection of gear fault,and the experimental system is composed of PCI-2 acoustic emission system and rotary machinery vibration fault simulation test bed. The accuracy of gear fault classification is 10% higher than that before optimization,which is very important for gear fault diagnosis.