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

Abstract

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.

Classification of surface roughness during turning of forged EN8 steel using vibration signal processing and support vector machine

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Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault Diagnosis

“…Considering that a single dissimilarity measure for the optimal subspace selection may not be able to capture all the characteristic information of the signals, both the relative entropy, which describes the difference of energy distribution in different classes of signals, and the normalized energy difference between different classes of signals are used as the dissimilarity measures in this study, which have been identified as good measures for classification. The details of these two measures can be seen in [30]. Features from these subspaces can then be extracted to distinguish different classes in a given set of data that belong to several classes.…”

Joint Feature and Model Selection for SVM Fault Diagnosis in Solid Oxide Fuel Cell Systems

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