Comparative Analysis of the RFT and IRIS Non-Destructive Testing Techniques Via Advanced Statistical and Artificial Intelligence Tools: the Case of Carbon Steel Tubular Bundles of Shell & Tube Heat Exchangers

Abstract This work presents a comparison between the Remote Field Testing (RFT) and Internal Rotary Inspection System (IRIS) non-destructive testing (NDT) techniques. Such a comparison aims to assess the influence of the inspector experience on the RFT inspection process, as well as to evaluate whether the RFT technique has an inspection capability equivalent to the IRIS test. By using inspection data, a statistical analysis was conducted based on the results from three different RFT inspectors. Then, considering a third technique (3D Scanning) applied to sections extracted from tubes (destructive case) as a reference basis, the statistical equivalence of both NDT techniques was evaluated. Finally, to assess the accuracy of the analyses conducted by the RFT inspectors and the comparison between the RFT and IRIS techniques, artificial intelligence (AI) tools were employed to measure the success and error rates of corrosion detection via the RFT case. The results show equivalence between the NDT techniques and, despite the interpretation challenges with the RFT case, the subjectivity by the inspectors with its inspection process was considered low. The AI classification results indicated that the generated network was capable of classifying the corrosion effectively, hence supporting the equivalence between the RFT and IRIS techniques.