基于改进萤火虫算法优化SVM的滚动轴承故障诊断  

Fault Diagnosis Method of Rolling Bearing Optimized by SVM Based on Improved Firefly Algorithm

作　　者：常梦容 王海瑞[1] 肖杨 王椿晶 蒋朝云 CHANG Meng-rong;WANG Hai-rui;XIAO Yang;WANG Chun-jing;JIANG Chao-yun(Faculty of Information Engineering and Automation,Kunming University of Science and Technology)

机构地区：[1]昆明理工大学信息工程与自动化学院

出　　处：《化工自动化及仪表》2021年第4期372-377,共6页Control and Instruments in Chemical Industry

基　　金：国家自然科学基金项目(61263023,61863016)。

摘　　要：针对萤火虫算法优化支持向量机时,收敛速度较慢、易陷入局部最小值等问题,将萤火虫算法进行改进来优化支持向量机,用此方法来对轴承故障进行诊断。经过对比实验,发现该方法分类效果和识别准确率均有一定的提高。Considering slow convergence speed and being easy to fall into the local minimum value while making use of firefly algorithm to optimize the support vector machine(SVM),having the firefly algorithm improved to optimize SVM was implemented for the bearing fault diagnosis.Comparative testing showed that,this method’s classification effect and recognition accuracy are improved to some extent.

关 键 词：改进萤火虫算法 支持向量机 故障诊断 滚动轴承 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]