一种新型多分类支持向量算法及其在故障诊断中的应用

New Multi-class Support Vector Algorithm and Its Application in Fault Diagnosis

层次支持向量机(H-SVM)比通常的多分类支持向量算法具有更快的训练速度和分类速度,便于实现在线分类。提出一种基于H-SVM的多类故障诊断方法,根据特征空间中各类故障样本中心之间的距离来逐层划分子类,距离较近的故障样本归为同一个子类进行训练,得到的H-SVM层次结构合理,各层的SVM分类间隔大、泛化性能强。另外,用ν-SVM代替通常的C-SVM作为两类分类器,分类器参数意义明确、变化范围小,更容易确定。针对一个涡轮喷气发动机气路部件故障诊断的仿真实验表明,设计的故障分类器具有良好的分类准确性和泛化性能,可以对发动机气路部件的典型故障进行快速诊断。作为应用实例,对JT9D发动机的6种实际故障进行了有效诊断。

Hierarchical support vector machines （H-SVMs） are faster in training and classifying than other usual multi-class SVMs, and therefore they are suitable for on-line fault diagnosis. A new multi-class fault diagnosis algorithm was proposed based on H-SVM. Before SVM training, the training data were first clustered according to their class center Euclid distances in some feature space. The patterns which have close distances were divided into the same sub-classes for training, and this made the SVMs have better generalization performance and reasonable hierarchical construction. Instead of common C-SVM, v-SVM was selected as binary classifier, in which the meaning of parameter v was more obvious and could be determined more easily. A simulation diagnosis experiment for the gas path components of a turbojet engine is conducted to demonstrate the effect of the algorithm. The simulation results show that the designed H-SVMs can fast diagnose 5 classes of single fault and 8 classes of combination fault for the engine. The fault classifiers have good accuracy and good generalization performance. As an application example, 6 kinds of real fault samples for JT9D engine were also classified correctly using the algorithm.