Defect profiling in steam generator tubes using multi-frequency eddy current inspection

Multi-frequency eddy current technique is one of the widely used Non-destructive evaluation techniques for steam generator tube inspection in nuclear power industry. The multi-frequency technique for depth profiling is essentially a multi-dimensional data fusion scheme which tends to mitigate the effect of error-prone single-dimension eddy current features due to noise and improves defect identification, classification and characterization of the eddy current data. In this research, the performance of traditional defect characterization algorithms are investigated alongside a novel defect depth profiling procedure using a radial basis function neural network by employing the following two step approach: (1) The length of the defect is estimated by setting an adaptive threshold on the magnitude distribution in the region of interest engulfing potential defects. (2) The inversion of the signal features in the defect region to predict a depth profile. The vehicle for this inversion is the radial basis function neural network. The thesis also discusses noise removal in eddy current data and their limitations when deployed for depth profiling purposes.