基于人工化学反应优化的SVM及旋转机械故障诊断  

SVM Based on ACROA and Its Applications to Rotating Machinery Fault Diagnosis

作　　者：罗颂荣[1,2] 程军圣[2] Hunglinh A O 

机构地区：[1]湖南文理学院,常德415003 [2]湖南大学,长沙410082

出　　处：《中国机械工程》2015年第10期1306-1312,共7页China Mechanical Engineering

基　　金：国家自然科学基金资助项目(51175158;51075131);湖南省教育厅科研项目(14C0789);湖南省"十二五"重点建设学科项目(湘教发2011[76])

摘　　要：针对支持向量机(SVM)的参数优化问题,结合人工化学反应优化算法的优点,提出了基于人工化学反应优化算法的支持向量机(ACROA_SVM)方法;然后利用标准数据验证了ACROA_SVM方法的有效性和优越性;最后,结合局部均值分解信号分析和能量矩特征提取,将ACROA_SVM方法应用于旋转机械故障诊断中。分析结果表明,ACROA_SVM方法不但具有较高的故障诊断精度和较好的泛化能力,而且时间消耗短,故障诊断效率高,有利于实现在线智能故障诊断。Firstly,in view of SVM parameters optimization problem,combination to the advantage of ACROA,a new classification model,called ACROA_SVM was presented herein.Furthermore,the effectiveness and superiority of the ACROA_SVM model was identified via benchmark datasets,which was downed from the sit web of UCI.Lastly,combination to local mean decomposition and energy moment feature extraction,ACROA_SVM was served as approach of pattern recognition to identify rotating machinery fault types.The experimental results show ACROA_SVM method has higher precision,better generalization ability of fault diagnosis,and less time consumption,higher efficiency of fault diagnosis,which is conducive to realize online intelligent fault diagnosis.

关 键 词：支持向量机 人工化学反应优化算法 旋转机械 故障诊断 

分 类 号：TH165.3[机械工程—机械制造及自动化] TN911.7[电子电信—通信与信息系统]