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《Chinese Journal of Aeronautics》 2006-03
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Fault Diagnosis for Aero-engine Applying a New Multi-class Support Vector Algorithm

XU Qi-hua1,SHI Jun2 (1. Electronic Engineering Department, Huaihai Institute of Technology, Lianyungang 222005,China) (2. College of Automatic Control, Northwestern Polytechnical University, Xi′an 710072,China)  
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 ν-SVM is selected as the binary classifier, in which the parameter ν 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.
【Fund】: University Science Foundation of Jiangsu Province (04KJD510018)
【CateGory Index】: V263.6
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