A System to Inspect Corrosion Under Insulation via Temporal Thermography & Artificial Intelligence

Abstract In this work, we present a novel Artificial Intelligence (AI) powered non-destructive testing (NDT) system for the detection of potential corrosion under insulation (CUI) inspections, code named DPCUI, developed by the Research and Development Center (R&DC) at Saudi Aramco in partnership with Baker Hughes. This inspection system enables fast external thermographic screening of large facilities by covering many condition monitoring locations (CML), and without any contact with the asset surfaces. It examines temporal thermography datasets that are collected using a high-resolution IR camera, such as those provided by FLIR, and on one or more RGB images that provide context of inspected areas. The collected data is analyzed by a dedicated AI engine to detect the presence of abnormal heat transfer signatures that occur due to defects present within the targeted CMLs. The novelty of this AI powered technology has several advantages. It provides a contact-less, smart, easy, fast, automated, safe, and reliable risk-based repair and maintenance decision making on the integrity of assets, enabling asset owners to efficiently prioritize their operations and processes in a seamless manner while the assets are kept online. Here, we enhance and extend the performance of our AI models to predict not only the presence of potential CUI or not, i.e. binary classification, but also various types of potential CUI, i.e. multi-class classification, a first of its kind.