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http://dx.doi.org/10.12673/jant.2014.18.4.304

Automatic Defect Detection and Classification Using PCA and QDA in Aircraft Composite Materials  

Kim, Young-Bum (Information & Telecommunication Engineering, Korea Aerospace University)
Shin, Duk-Ha (Information & Telecommunication Engineering, Korea Aerospace University)
Hwang, Seung-Jun (Information & Telecommunication Engineering, Korea Aerospace University)
Baek, Joong-Hwan (Information & Telecommunication Engineering, Korea Aerospace University)
Publication Information
Journal of Advanced Navigation Technology / v.18, no.4, 2014 , pp. 304-311 More about this Journal
Abstract
In this paper, we propose a ultra sound inspection technique for automatic defect detection and classification in aircraft composite materials. Using local maximum values of ultra sound wave, we choose peak values for defect detection. Distance data among peak values are used to construct histogram and to determine surface and back-wall echo from the floor of composite materials. C-scan image is then composed through this method. A threshold value is determined by average and variance of the peak values, and defects are detected by the values. PCA(principal component analysis) and QDA(quadratic discriminant analysis) are carried out to classify the types of defects. In PCA, 512 dimensional data are converted into 30 PCs(Principal Components), which is 99% of total variances. Computational cost and misclassification rate are reduced by limiting the number of PCs. A decision boundary equation is obtained by QDA, and defects are classified by the equation. Experimental result shows that our proposed method is able to detect and classify the defects automatically.
Keywords
Defect detection; classification; PCA; QDA; Aircraft composite material;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
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