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Mathematical Problems in Engineering
Volume 2014, Article ID 283718, 9 pages
Research Article

Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine

1School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
2Changcheng Institute of Metrology and Measurement, Aviation Industry Corporation of China, Key Laboratory of Science and Technology on Metrology & Calibration, Beijing 100095, China

Received 30 April 2014; Accepted 10 June 2014; Published 26 June 2014

Academic Editor: Weihua Li

Copyright © 2014 Jianwei Cui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB) with support vector machine (SVM). The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT-) based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.