Steam generator automated eddy current data analysis: A benchmarking study. Final report

The eddy current examination of steam generator tubes is a very demanding process. Challenges include: complex signal analysis, massive amount of data to be reviewed quickly with extreme precision and accuracy, shortages of data analysts during peak periods, and the desire to reduce examination costs. One method to address these challenges is by incorporating automation into the data analysis process. Specific advantages, which automated data analysis has the potential to provide, include the ability to analyze data more quickly, consistently and accurately than can be performed manually. Also, automated data analysis can potentially perform the data analysis function with significantly smaller levels of analyst staffing. Despite the clear advantages that an automated data analysis system has the potential to provide, no automated system has been produced and qualified that can perform all of the functions that utility engineers demand. This report investigates the current status of automated data analysis, both at the commercial and developmental level. A summary of the various commercial and developmental data analysis systems is provided which includes the signal processing methodologies used and, where available, the performance data obtained for each system. Also, included in this report is input from seventeen research organizations regarding the actions required and obstacles to be overcome in order to bring automatic data analysis from the laboratory into the field environment. In order to provide assistance with ongoing and future research efforts in the automated data analysis arena, the most promising approaches to signal processing are described in this report. These approaches include: wavelet applications, pattern recognition, template matching, expert systems, artificial neural networks, fuzzy logic, case based reasoning and genetic algorithms. Utility engineers and NDE researchers can use this information to assist in developing automated data analysis systems in an efficient and cost effective manner, by gaining an understanding of those methods that have produced the most promising results to date, as well as by learning of those approaches that have not been successful