基于IEM-FA优化LSSVM的风机主轴轴承故障诊断研究  

Research on Wind Turbine Main Shaft Bearing Fault Diagnosis Based on IEM-FA Optimized LSSVM

作　　者：石志标[1] 姜红阳 SHI Zhi-biao;JIANG Hong-yang(School of Mechanical Engineering,Northeast Electric Power University,Jilin Jilin 132012,China)

机构地区：[1]东北电力大学机械工程学院,吉林吉林132012

出　　处：《组合机床与自动化加工技术》2019年第1期90-93,共4页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家自然科学基金(51576036);吉林省科技发展计划项目(20100506)

摘　　要：针对基本萤火虫算法(FA)易陷于局部最优值和搜索速度慢的问题,文章提出了一种改进的萤火虫算法(IEM-FA)。在种群迭代过程中加入振荡因子更新固定步长和细化扰动项,并利用IEMFA算法优化最小二乘支持向量机(LSSVM)的参数。测试结果表明,IEM-FA算法优化LSSVM的诊断模型模型可以准确、高效地对风机主轴轴承进行故障诊断。According to the problem that the firefly algorithm(FA)is prone to local optimal value,and the slow speed of searching,an improved firefly algorithm(IEM-FA)is proposed.In the process of population iteration,oscillation factor was added to update the fixed step size and refine the disturbance term,and the parameters of the Least Squares Support Vector Machine(LSSVM)were optimized using the IEMFA algorithm.The test results show that the diagnosis model of LSSVM optimized by IEM-FA algorithm can diagnose the failure of the wind turbine main shaft bearing accurately and effectively.

关 键 词：最小二乘支持向量机 参数优化 萤火虫算法 故障诊断 

分 类 号：TH162[机械工程—机械制造及自动化] TG506[金属学及工艺—金属切削加工及机床]