基于SVM 的二叉树多类分类算法及其在故障诊断中的应用

基于结构风险最小化原则的支持向量机(SVM)对小样本决策具有较好的学习推广性。但由于常规SVM算法是从2类分类问题推导出的，在解决故障诊断这种典型的多类分类问题时存在因雄，因而提出一种依赖故障优先级的基于SVM的二叉树多级分类器实现(2PTMC)方法，该方法具有简单、直观，重复训练样本少的优点。通过将其应用于柴油机振动信号的故障诊断，获得了令人满意的效果。

2PTMC classification algorithm based on support vector machines and its application to fault diagnosis

Support vector machines is a new general machine learning tool based on structural risk minimization principle that exhibits good generalization. Fault diagnosis based on support vector machines is discussed. Since SVM was originally designed for binary classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification named 2PTMC is presented. This classifier is a binary tree classifier composed of several SVMs organized by fault priority, which is simple and has little duplicating training samples. The application to fault diagnosis for diesel engine shows the effectiveness of the method.