基于网格支持矢量机的涡轮泵多故障诊断

MULTI-FAULT DIAGNOSIS FOR TURBO-PUMP BASED ON MESH SUPPORT VECTOR MACHINES

支持矢量机是一种基于结构风险最小化原则的机器学习方法,对小样本决策具有较好的学习推广性。由于常规支持矢量机算法是从二类分类问题推导得出的,在解决故障诊断这种典型的多类分类问题时存在困难,为此提出一种网络支持矢量机多类分类算法,用每个类别和其他两个至四个类别构造二类支持矢量机分类器。这些二类支持矢量机分类器组合而成的网格式结构多类分类器,具有容易扩展、重复训练样本少、速度快和识别正确率高的优点。将网格式结构多类分类器应用于涡轮泵试验台多故障诊断获得了令人满意的效果。

Support vector machine（SVM） is a new general machine-learning tool based on structural risk minimization principle that exhibits good generalization even when fault samples arc few. Fault diagnosis based on support vector machines is discussed. Since basic support vector machines is originally designed for two-class classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification algorithm named mesh support vector machines is presented to solve the multi-class recognition problems. It is a mesh classifier in which every class constructs two-class SVM classifiers with less than 4 other classes. It is simple and extensible, and has little repeated training amount, so the rate of training and recognition is expedited. The effectiveness of the method is verified by the application to the multi-fault diagnosis for turbo pump test bed.