1. Digital eddy current test data calibration, compression and representation;
2. flaw classification and decision confidence estimation using fuzzy logic;
3. development of robust neural networks for tube defect sizing;
4. system integration for database management, compilation of a trained neural network library, and a decision module; and
5. performance evaluation of the integrated system using eddy current test data.

This integrated approach for automating the analysis of eddy current test data for steam generator tubing diagnosis is an original contribution of this research project. The fuzzy logic based flaw detection system is the first to utilize information from multi-frequency eddy current data for flaw detection. A WINDOWS-based expert system, called EDDYAI, was developed using Visual C++. This system integrates all the analysis modules into a user-friendly expert system. The system has been tested extensively using ECT data to establish its ability to identify defect types and to estimate defect parameters.