重庆大学学报2018，Vol.41Issue(1)：99-107,9.DOI:10.11835/j.issn.1000-582X.2018.01.011
SVM与PSO相结合的电机轴承故障诊断
Fault diagnosis of motor bearings based on SVM and PSO
摘要
Abstract
A fault diagnosis method for motor bearings based on support vector machine (SVM) and particle swarm optimization (PSO) is proposed.The characteristic of the vibration signal is characterized by the time-domain and the wavelet packet energy characteristics,which makes the characteristic of the vibration signal has good reliability and sensitivity and improves the accuracy of fault diagnosis.The PSO algorithm is used to optimize the parameters of the penalty parameter and the radial basis kernel function of SVM,and compared with other parameter-optimization algorithms.Experimental results show that the proposed bearing fault diagnosis method has a very good effect not only on the recognition of motor bearing outer race fault,inner race fault and ball fault,but also on the severity differentiation of every kind of fault.It has strong practicability.关键词
支持向量机/粒子群优化算法/小波包分析/特征提取/电机轴承/故障诊断Key words
support vector machine/particle swarm optimization algorithm/wavelet packet analysis/feature extraction/motor bearing/fault diagnosis分类
机械制造引用本文复制引用
李嫄源,袁梅,王瑶,程安宇..SVM与PSO相结合的电机轴承故障诊断[J].重庆大学学报,2018,41(1):99-107,9.基金项目
重庆市科技人才培养计划资助项目(CSTC2013KJRC-TDJS40010).Supported by Science and Technology Personnel Training Program of Chongqing (CSTC2013KJRC-TDJS40010). （CSTC2013KJRC-TDJS40010）
