In-service inspection of complex systems such as nuclear steam generator (SG) tubes and their surrounding support structures is challenged by overlapping degradation modes. In these complex systems the simultaneous and accurate measurement of more than two interdependent parameters is difficult using standard statistical regression analysis tools. Recently, artificial neural networks (ANNs) have been investigated for dealing with the complex relation between inspection data and defect properties. In this paper, pulsed eddy current data were obtained using a single driver with an array of eight pick-up coils configured for inspection of Alloy-800 SG tube fretting, accompanied by tube offset within a simulated corroding ferromagnetic support structure. Time-voltage data were processed by a modified principal component analysis (MPCA) to reduce data dimensionality, and MPCA scores were input into an ANN that simultaneously targeted four parameters associated with support structure hole size, tube off-centering in two dimensions, and fret depth. The neural network was trained, tested, and validated on experimental data and provided estimates within 2% of hole inner diameter (ID) and 3% of fret depth targets. The estimates of hole ID and tube position were further improved when fret depth was used as an input, as might occur if fret depth inspection results are available.
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Buck et al. (2016) studied this question.