支持向量机及其在机械故障诊断中的应用研究

针对支持向量机分类算法中模型选择对分类精确性影响很大的问题，结合转子实验台模拟的典型旋转机械故障数据对影响多故障分类器分类性能的相关因素进行了研究。结果表明，在少量时域故障数据样本条件下，选用不同的核函数及核函数参数对多故障分类器的分类精度有一定影响，为实际工程应用中选择合适的支持向量机核函数类型及其参数提供一定的帮助.

Support Vector Machine and Its Ications Research in Machine Fault Diagnosis

Aiming at the difficulty that Support vector machine (SVM) model selection of classification algorithm affect Classification accura-cy. It research relevant factors that influence the precision of fault classifiers based on the typical fault data samples obtained by rotor experi-mental table. The results show that different SVM classifiers, in which different kernel functions and different kernel functions parameters are adopted ,will influence the precision of fault classifiers in conditions that fault data samples is small. It can be conveniently applied to choose appropriate kernel functions and kernel functions parameters in the practical aengineering pplication.