Artificial Intelligence for Assisted Analysis of Eddy Current Data from Heat Exchangers with Non-Ferromagnetic Tubes

Abstract

Oil & Gas and petrochemical industries need regular inspection of shell and tube heat exchangers to maintain performance. NDT inspection service providers are required to complete the data collection and analysis in the shortest possible time frame, but manual analysis for defects like corrosion or pitting is time-consuming and prone to errors. This leads to safety risks and unexpected downtime, and there's a shortage of experienced ECT level II/III certified analysts globally. The industry needs tools to simplify data interpretation and increase analysis efficiency while maintaining a consistent and repeatable level of confidence. While assisted analysis for tube ECT testing has existed for several years, most solutions have been developed for the nuclear industry and employ methods that become quickly limited when the test conditions are not well controlled or when the tube bundle information is incomplete These situations are very common outside the nuclear industry, and they often contribute to reducing the efficiency and reliability of data interpretation. This communication outlines the use of AI in various tasks of tube data analysis, from landmark localization to indication detection, to improve efficiency and confidence in the analysis process. A complete analysis system has been implemented and field data results demonstrate the system's value. Robustness to suboptimal data quality is also discussed.