Fault Diagnosis for Aero-engine Applying a New Multi-class Support Vector Algorithm

• Published in: Chinese journal of aeronautics Vol. 19; no. 3; pp. 175 - 182
• Main Authors: XU, Qi-hua, SHI, Jun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2006
• Subjects: fault diagnosis; multi-class classification; support vector machine; fault diagnosis; multi-class classification; support vector machine
• ISSN: 1000-9361
• DOI: 10.1016/S1000-9361(11)60342-7

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.