Application of principal component analysis in defect identification using pulsed eddy current method

In the pulsed eddy current testing (PEC) of steel structures, multiple characteristics such as peak amplitude, zero-cross time, and peak area etc., are usually used for defect characterization. However, information redundancy existing among the above characteristics increases the amount of data for the analysis and the difficulty of information filtering; this could influence the efficiency of defect identification. In order to reduce information redundancy, principal component analysis (PCA) was employed to compress the six characteristics into one principal component characteristic which was then used as input for Logistic classifier to identify thinning defects in steel structure. The results show that PCA can effectivly reduce the amount of data processed by classifier and improve the efficiency of defect identification while ensuring accuracy.