A novel feature extraction for eddy current testing of steam generator tubes

Abstract

For defect characterization in steam generator tubes, feature extraction to interpret eddy current testing (ECT) signals has been recognized as an important step. In this paper, we propose a new feature extraction from ECT signals for defect classification and defect sizing. Using the extracted features as an input vector, a multi-layer perceptron (MLP) neural networks are used to classify defect types and to predict defect size. Although the proposed method requires relatively fewer features for the defect classification, it provides not only a high level of classification accuracy but also promising robustness to noise. Moreover, for the prediction of defect size, the proposed method yields a comparable prediction accuracy even though it needs fewer features than the previous result.