Abstract
Principal components analysis (PCA) involves transforming a set of correlated observations into a set of linearly uncorrelated variables, which can reveal simplified trends in data. Multifrequency eddy current testing contains correlations across different test frequencies. In this paper, PCA is used to extract unique information from multifrequency eddy current data sets, used to measure the pressure tube to calandria tube gap, in CANada Deuterium Uranium fuel channels. Advantages include compressed data acquisition, allowing for increased inspection speed, and monitoring for variation in physical parameters using a reduced number of variables. PCA employing analytical input model data is validated against PCA employing data from physical experiments.
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