机械与电子2019，Vol.37Issue(1)：42-48,7.
改进粒子群算法优化的支持向量机在滚动轴承故障诊断中的应用
Application of SVM Optimized by IPSO in Rolling Bearing Fault Diagnosis
摘要
Abstract
Aiming at the problem that the classification effect of support vector machine (SVM) is not satisfactory due to improper selection of penalty factor Cand kernel parameter g, a new modified classifier that uses the improved particle swarm optimization (IPSO) was proposed to optimize the parameter of SVM (IPSO-SVM) by introducing the dynamic inertia weight, global neighborhood search, population shrinkage factor and particle mutation probability.The classification result was verified by common data sets named BreastTissue, Heart and Wine from the Libsvm toolbox, the results show that IPSO-SVM classifier is obviously superior to SVM and PSO-SVM classifier in terms of prediction accuracy and classification time.Then it was applied to the fault diagnosis in two classification problems and multiple classification problems of rolling bearings.The simulation results show that the IPSO-SVM classifier has stronger global convergence ability and faster convergence speed, and the ideal classification results can be obtained.Finally, the IPSO-SVM classifier was used to diagnose the fault of the actual bearing.The results show that the classifier has a better classification stability and is worthy of further promotion in engineering field.关键词
支持向量机/参数优化/改进粒子群算法/滚动轴承/故障诊断Key words
support vector machine/parameter optimization/improved particle swarm optimization/rolling bearing/fault diagnosis分类
信息技术与安全科学引用本文复制引用
吕明珠,苏晓明,陈长征,刘世勋..改进粒子群算法优化的支持向量机在滚动轴承故障诊断中的应用[J].机械与电子,2019,37(1):42-48,7.基金项目
国家自然科学基金资助项目(51675350) （51675350）
高校应用性研究专项课题(2018YYYJ-3) （2018YYYJ-3）
高校重点课题(2018XB01-4) （2018XB01-4）
