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Aero-engine interior damage recognition based on support vector machine

MENG Jiaoru(College of Electric and Information Engineering,Heilongjiang Institute of Science and Technology,Harbin 150027,China)  
Targeted at the failure of most of the current aero-engine borescopic inspection system to identify the kind of interior damages automatically,this paper introduces a new damage recognition method which combines support vector machine(SVM) with borescopic inspection technology.The method consists of converting the damage image to a binary image,extracting five shape features and four texture features from the chain-code and gray-level co-occurrence matrix of the image respectively and putting these features into SVM to carry out automatic classification of damages.The design of the classifier involves the development of a high-performance binary tree-SVM which decreases the number of training sample and improves the efficiency of SVM.CFM56 aero-engine shows a higher recognition accuracy than traditional SVM multi-class method and BP neural network method.
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