The research on the flaw classification and identification of the PEC NDT technology

Abstract

Pulsed Eddy Current (PEC) NDT has played a vital role in detection and classification of the surface and sub-surface defects in conductive structures. Normally, it uses peak values of the acquired transient field signals, and the combina tion of the feature values of the time of the peak to identify flaws wit h the help of Principal Component Analysis (PCA). However, it is found that the random noise undermines the classification results, because PCA works robustly only in the time domain. In the light of this drawback, the fundamental and the first-har monic components are investigated and taken as the new feature values in the frequency domain. Through the analysis of the feature values in both time and frequency domains, the influen ce of random noise is mitigated. Consequently, surface defects, subsurface defects and metal thickness changes are classified with much higher identification accuracy.