Eddy Current Signal Deconvolution Technique for the Improvement of Steam Generator Tubing Burst Pressure Predictions

Abstract

Eddy current techniques are extremely sensitive to the presence of axial cracks in nuclear power plant steam generator tube walls, but they are equally sensitive to the presence of dents, fretting, support structures, corrosion products, and other artifacts. Eddy current signal interpretation is further complicated by cracking geometries more complex than a single axial crack. Although there has been limited success in classifying and sizing defects through artificial neural networks, the ability to predict tubing integrity has, so far, eluded modelers. In large part, this lack of success stems from an inability to distinguish crack signals from those arising from artifacts. We present here a new signal processing technique that deconvolves raw eddy current voltage signals into separate signal contributions from different sources, which allows signals associated with a dominant crack to be identified. The signal deconvolution technique, combined with artificial neural network modeling, significantly improves the prediction of tube burst pressure from bobbin-coil eddy current measurements of steam generator tubing.