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 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.
提出了一种基于核的多类别模式识别算法(简称核子空间法，KSPM)，依据此算法建立了多故障分类器。
A novel multi-class classifier with kernels, namely kernel Subspace Methods (KSPM), was presented, and a multi-fault classifier based on the algorithm was constructed.
该方法将振动信号小波包分解后的频带能量作为特征向量，输入到由多个支持向量机构成的多故障分类器中进行故障识别和分类。
According to the method, the energy of different frequency bands after wavelet packet decomposition constitutes the input vectors of support vector machine as feature vectors.
该方法将振动信号小波包分解后的频带能量作为特征向量，输入到由多个支持向量机构成的多故障分类器中进行故障识别和分类。
According to the method, the energy of different frequency bands after wavelet packet decomposition constitutes the input vectors of support vector machine as feature vectors.
应用推荐