Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine
Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine
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DOI：
10.1155/2014/283718
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发表时间：
2014-06
影响因子：
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通讯作者：
Jianwei Cui;Mengxiao Shan;Ruqiang Yan;Ya-hui Wu
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文献类型：
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作者：
Jianwei Cui;Mengxiao Shan;Ruqiang Yan;Ya-hui Wu
针对航空发动机碰摩故障，提出了一种改进的局部判别基（LDB）与支持向量机（SVM）相结合的有效诊断方法。采用改进的LDB算法，以归一化能量差和相对熵作为量化指标，选择最优正交子空间集，进行基于小波包变换（WPT-）的信号分解。然后得到两组最优的正交子空间，并从这两组子空间中提取的能量特征将被选择作为SVM分类器的输入，以诊断航空发动机故障。在某型航空发动机碰摩试验系统上进行的试验研究验证了该方法对航空发动机工况分类的有效性。
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.