单形进化算法优化的SVM滚动轴承故障诊断  

Rolling bearing fault diagnosis of SVM optimized by surface-simplex swarm evolution

作　　者：郑蒙福 全海燕[1] ZHENG Mengfu;QUAN Haiyan(College of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650504,P.R.China)

机构地区：[1]昆明理工大学信息工程与自动化学院,昆明650504

出　　处：《重庆大学学报》2021年第2期43-52,共10页Journal of Chongqing University

基　　金：国家自然科学基金项目(41364002)。

摘　　要：由于支持向量机的参数优化算法存在控制参数多、易陷入局部最优解的问题,提出了一种单形进化算法优化的支持向量机滚动轴承故障诊断方法。首先,单形进化算法利用全随机的方式建立粒子的单形邻域搜索算子以减少算法控制参数,建立粒子多角色态搜索策略以避免算法陷入局部最优解;然后,将单形进化算法应用于支持向量机的参数寻优,并用滚动轴承信号完成故障诊断;试验中,采用滚动轴承信号的集总经验模态分解的能量特征作为输入,进行算法的性能分析与测试。结果表明该算法可以有效地缓解粒子陷入局部最优解,且减少了控制参数,并能完成滚动轴承故障信号的诊断与识别。To solve the problem that the parameter optimization algorithm of support vector machine(SVM)has many control parameters and easily falls into local optimum,a rolling bearing fault diagnosis method of SVM optimized by surface-simplex swarm evolution algorithm is proposed.First,surface-simplex swarm evolution(SSSE)is used to establish the particle’s simple neighborhood search operator in a random way to reduce control parameters and develop the multi-role state search strategy to avoid falling into the local optimum.Then,SSSE is applied to parameter optimization of SVM to realize fault diagnosis.In the test,the energy matrix of ensemble empirical mode decomposition(EEMD)of rolling bearing signal is used as the feature input to perform performance analysis and testing of the method.The results show that the method not only effectively avoids the particles falling into the local optimal solution,but also reduces the control parameters,and can complete the signal diagnosis.

关 键 词：单形进化算法 支持向量机 参数优化 故障诊断 集总经验模态分解 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]