A new method of fault classification for mechanical system by means of support vector machine (SVM) is proposed and a multi-class SVM classifier based on binary classification was developed.
提出了一种利用支持向量机(SVM)对机械系统故障进行分类的新方法；以二值分类为基础，开发了基于支持向量机的多值分类器。
The twin screw extruder fault diagnosis by multi-fault classifier based on SVM is mainly discussed and the retest proves that this SVM really has preferable ability of classification.
诊断实例表明，基于支持向量机的多故障分类器对设备故障具有很好的分类效果。
The model categorizes the vibration fault according to its attributes, Narrows the range for searching fault by classification, then determines the fault by comprehensive multi-factor judgement.
该模型是按照故障征兆属性归类，通过分类识别缩小故障搜寻范围以利于故障的模糊诊断，然后再进行因子综合判断，对振动故障诊断的方法进行了研究。
The model categorizes the vibration fault according to its attributes, Narrows the range for searching fault by classification, then determines the fault by comprehensive multi-factor judgement.
该模型是按照故障征兆属性归类，通过分类识别缩小故障搜寻范围以利于故障的模糊诊断，然后再进行因子综合判断，对振动故障诊断的方法进行了研究。
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