CAJ | 학술논문

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教类型及其参数提供一定的帮助.
침대지지향량궤분류산법중모형선택대분류정학성영향흔대적문제,결합전자실험태모의적전형선전궤계고장수거대영향다고장분류기분류성능적상관인소진행료연구.결과표명.재소량시역고장수거양본조건하,선용불동적핵함수급핵함수삼수대다고장분류기적분류정도유일정영향,위실제공정응용중선택합괄적지지향량궤핵함교류형급기삼수제공일정적방조.
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.