Composite defects diagnosis using parameter optimization based support vector machine

This study concerns with the diagnosis of composite defects using pitch-catch method in aircraft material by applying the Wavelet transform (WT) analysis, PCA along with support vector machine (SVM). A novel application is presented exploring the problem of detection and estimation of the various defects; the early detection and classification of aircraft defects is of particular importance, as the defects can lead to severe problem due to material failure, component analysis was performed initially to extract the features and to reduce the dimension of the original data features. Kernel parameters selection of support vector machine is a very important problem, which has great influence on the performance of support vector machine. This paper exploits the parameter optimization procedure to ensure the generalization ability of SVM. The result shows that multi-class SVM produces promising results and has the potential for use in fault diagnosis.