中国机械工程
中國機械工程
중국궤계공정
CHINA MECHANICAl ENGINEERING
2015年
10期
1306-1311,1312
,共7页
罗颂荣%程军圣%Hunglinh AO
囉頌榮%程軍聖%Hunglinh AO
라송영%정군골%Hunglinh AO
支持向量机%人工化学反应优化算法%旋转机械%故障诊断
支持嚮量機%人工化學反應優化算法%鏇轉機械%故障診斷
지지향량궤%인공화학반응우화산법%선전궤계%고장진단
support vector machine (SVM )%artificial chemical reaction optimization algorithm (ACROA)%rotating machinery%fault diagnosis
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。
針對支持嚮量機(SVM )的參數優化問題,結閤人工化學反應優化算法的優點,提齣瞭基于人工化學反應優化算法的支持嚮量機(ACROA_SVM )方法;然後利用標準數據驗證瞭ACROA_SVM 方法的有效性和優越性;最後,結閤跼部均值分解信號分析和能量矩特徵提取,將ACROA_SVM 方法應用于鏇轉機械故障診斷中。分析結果錶明,ACROA_SVM 方法不但具有較高的故障診斷精度和較好的汎化能力,而且時間消耗短,故障診斷效率高,有利于實現在線智能故障診斷。
침대지지향량궤(SVM )적삼수우화문제,결합인공화학반응우화산법적우점,제출료기우인공화학반응우화산법적지지향량궤(ACROA_SVM )방법;연후이용표준수거험증료ACROA_SVM 방법적유효성화우월성;최후,결합국부균치분해신호분석화능량구특정제취,장ACROA_SVM 방법응용우선전궤계고장진단중。분석결과표명,ACROA_SVM 방법불단구유교고적고장진단정도화교호적범화능력,이차시간소모단,고장진단효솔고,유리우실현재선지능고장진단。
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 en-ergy moment feature extraction ,ACROA_SVM was served as approach of pattern recognition to iden-tify 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 ,w hich is conducive to realize online intelligent fault diagnosis .