An integrated signal processing and neural networks system for steam generator tubing diagnostics using eddy current inspection

Abstract

The primary purpose of this research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive evaluation (NDE) data. Specifically, data from eddy current inspection of steam generator tubing were utilized to evaluate this technology. The focus of the research was to develop and test various data compression methods (for eddy current data) and the performance of different neural network paradigms for defect classification and defect parameter estimation. Feedforward, fully-connected neural networks, that use the back-propagation algorithm for network training, were implemented for defect classification and defect parameter estimation using a modular network architecture. A large eddy current tube inspection database was acquired from the Metals and Ceramics Division of Oak Ridge National Laboratory (ORNL). These data were used to study the performance of artificial neural networks for defect type classification and for estimating defect parameters. Most of the study was made using the NeuralWorks Professional II/Plus software. A PC-based data pre-processing and display program was also developed as part of an expert system for data management and decision making. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods. The integration of data compression and artificial neural networks for information processing was established as an efficient technique for automation of diagnostics using nondestructive evaluation methods.