The results from the experiments prove that the SVM method has a good classification ability and high efficiency for multi-fault classification in mechanical systems, even for the cases without preprocessing of the original signal.
结果表明 ,该方法具有很好的分类能力和较高的计算效率 ,不需要对原始数据进行预处理就可达到满意的效果 ,可以满足在线诊断的要求 ,适合于机械故障诊断中的多故障分类。 该方法的应用 ,为故障诊断技术向智能化方向发展提供了新的途径。
参考来源 - 基于支持向量机的机械系统多故障分类方法·2,447,543篇论文数据，部分数据来源于NoteExpress
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。
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