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

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.