运用改进二叉树SVM算法的柴油机振动诊断

提出了一种改进的二叉树支持向量机(support vector machines,简称SVM)算法,用以克服二叉树SVM构造时各级正类样本选择缺乏理论指导的问题。基于最易分割类应最先分割的思想,该方法在定义特征参数类识别率概念的基础上,首先逐级计算每个特征参数对各级SVM所对应各类训练样本的类识别率,然后选择类识别率综合排序结果处于第1位的样本类作为相应级SVM的正类。从缸盖振动信号包络幅值域参数和小波包分解频带能量百分比参数中选取了对气阀故障较为敏感的9个特征,形成了诊断特征向量,使用常用的1-a-r,1-a-1,DDAG以及改进的二叉树SVM多分类方法对6种气阀间隙状态进行了诊断,结果表明,本文提出的改进二叉树SVM方法具有最好的分类效果。

Diesel Vibration Diagnosis Using Improved Binary Tree Support Vector Machine Algorithm

Abstract The paper presents an improved binary tree-based SVM multi-class classification algorithm. Guided by the principle for those class samples most easily to be separated should be divided firstly, the method, after defining class recognition rates of feature parameters, calculates level-by-level the class recognition rates of each feature against each class training samples of the corresponding level SVM; Then, according to the synthetic sorting result of the class recognition rates, chooses the class placed first as the positive class of the corresponding level SVM. From the cylinder head vibration signal envelopes, nine sensitive temporal domain parameters and wavelet package frequency band power percentage parameters are extracted and formed the diagnosis feature vector. The proposed binary tree SVM, as well as the commonly used 1-a-r (one-against-rest), 1-a-1 (one-against-one) and DDAG (Decision Directed Acyclic Graph) multi-class SVM classification method, are respectively used to recognize six simulative diesel valve gap states. Result shows, among the four multi-class classification methods, the proposed strategy has the best classification precision.