基于人工化学反应优化的SVM及旋转机械故障诊断

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。

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

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.