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基于新型多分类支持向量算法的发动机故障诊断
引用本文:徐启华,师军.基于新型多分类支持向量算法的发动机故障诊断[J].中国航空学报,2006,19(3):175-182.
作者姓名:徐启华  师军
作者单位:[1]Electronic Engineering Department, Huaihai Institute of Technology, Lianyungang 222005, China [2]College of Automatic Control, Northwestern Polytechnical University, Xi'an 710072, China
基金项目:University Science Foundation of Jiangsu Province (04KJD510018)
摘    要:层次支持向量机(H-SVM)比通常的"一对多" (1-V-R)和"一对一" (1-V-1)等多分类支持向量算法具有更快的训练速度和分类速度.提出一种基于H-SVM的航空发动机气路部件故障诊断方法,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、泛化能力强;同时,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.仿真实验表明,基于H-SVM的故障分类器具有良好的分类准确性和泛化性能,能够对发动机气路部件的单一故障和复合故障进行快速诊断.

关 键 词:支持向量机  故障诊断  多类分类  support  vector  machine  fault  diagnosis  multi-class  classification
文章编号:1000-9361(2006)03-0175-08
收稿时间:2005-08-15

Fault Diagnosis for Aero-engine Applying a New Multi-class Support Vector Algorithm
XU Qi-hua,SHI Jun.Fault Diagnosis for Aero-engine Applying a New Multi-class Support Vector Algorithm[J].Chinese Journal of Aeronautics,2006,19(3):175-182.
Authors:XU Qi-hua  SHI Jun
Institution:1. Electronic Engineering Department, Huaihai Institute of Technology, Lianyungang 222005, China;2. College of Automatic Control, Northwestern Polytechnical University, Xi''an 710072, China
Abstract:Hierarchical Support Vector Machine (H-SVM) is faster in training and classification than other usual multi-class SVMs such as “1-V-R” and “1-V-1”. In this paper, a new multi-class fault diagnosis algorithm based on H-SVM is proposed and applied to aero-engine. Before SVM training, the training data are first clustered according to their class-center Euclid distances in some feature spaces. The samples which have close distances are divided into the same sub-classes for training, and this makes the H-SVM have reasonable hierarchical construction and good generalization performance. Instead of the common C-SVM, the v-SVM is selected as the binary classifier, in which the parameter v varies only from 0 to 1 and can be determined more easily. The simulation results show that the designed H-SVMs can fast diagnose the multi-class single faults and combination faults for the gas path components of an aero-engine. The fault classifiers have good diagnosis accuracy and can keep robust even when the measurement inputs are disturbed by noises.
Keywords:support vector machine  fault diagnosis  multi-class classification
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