A Novel Method Based on Support Vector Machine for Pipeline Defect Identification

Based on introducing the basic theory and principle of support vector machine (SVM), after de-noising the ultrasonic echo signals using wavelet transform and with a view of data mining, a novel approach using SVM classification is discussed to identify the defects. The experiment results show that unlike conventional and artificial neural networks (ANN) identification methods the new technique performs better than conventional evaluation ones with advantages of high efficiency, lower cost, easy implement on-line, excellent generalization. The approach provides a novel technique means for nondestructive defect identification of various defects.