Automated analysis of rotating probe multi-frequency eddy current data from steam generator tubes

An algorithm is presented for the automated analysis of rotating probe multifrequency eddy current data obtained from nuclear power plant steam generator tubes (SGT). The algorithm consists of four steps, namely, a preprocessing stage for conditioning the data, a decision tree based feature extraction stage for identifying relevant features for analysis, a neural network based classification stage for identifying signals from various defect types and benign structures, and finally a blind deconvolution based characterization stage for accurately estimating the size and orientation of the detected defects. This algorithm is optimized to maximize the probability of detection (POD), while keeping the number of false alarms (PFA) at a minimum. Initial results presented in this paper look very promising and demonstrate the effectiveness of the proposed algorithm.