机械故障特征与分类器的联合优化

Joint Optimization of the Fault Feature and Class ifier

在机械故障诊断中,特征选择和分类器的参数优化都可以提高诊断精度。利用特征和分类器参数的依赖关系,提出了特征选择和SVM参数的联合优化方法来提高诊断性能。联合优化方法采用支持向量机(SVM)作为故障分类器,SVM半径—间距上界(RM界)为目标计算诊断精度,并应用遗传算法求解此优化问题。齿轮故障诊断试验结果表明,联合优化的诊断精度要优于单独优化特征和SVM参数,而且优化速度更快。因此在故障诊断中,利用特征和分类器参数联合优化能够快速取得较好的诊断精度。

Feature selection and parameters optimization of the fault classifier can enhance the fault diagnosis accuracy. Using the interdependent relationship between the feature selection and classifier parameter, a method of joint optimization of feature selection and classifier parameters is proposed to improve the diagnosis accuracy. By using the method we adopt the support vector machine (SVM) as a fault classifier, take into account of the radius-margin bounds for the accuracy evaluation of SVM classifier, and applies genetic algorithm (GA) to solve the joint optimization problem. In the gear fault diagnosis experiment, the joint optimization method guarantees better diagnosis accuracy and the optimization process has a higher rate than the single optimization of features or SVM parameters. So the joint optimization of fault features and classifier can fast achieve the better diagnosis accuracy in fault diagnosis.