Data analysis of shell and tube heat exchangers (HX) has been around for decades yet continues to remain a challenge. The effectiveness and importance of inspection has been proven, but it still requires specialized training, is time intensive, and many parameters influence the data interpretation such as support structures, probe wobble, and geometry variations. Further to this, an unfound defect may have serious consequences: fluid contamination or leaks, decreased heat transfer efficiency, undesired chemical reactions, expensive downtime, as well as environmental and safety risks.
In an effort to reduce these sources of misinterpretation, conventional detection tools relying on user-defined rules have been successfully qualified and deployed in the nuclear industry. Although these tools are useful for large HX, they require intensive pre-inspection preparation work which makes them less suitable for smaller HX.
Over the past decades, AI has made major breakthroughs in various industries allowing computers to perform actions that were once only reserved for the human brain. This modern technology is a major technological revolution that will allow computers to perform various complex tasks in many aspects of life, such as self-driving cars, facial recognition technology and transcription app being used in smartphones.
To remain at the forefront of technology and to help the industry improve the assessment of HX around the world, by combining the expertise of AI specialists and eddy current testing (ECT) experts, Eddyfi Technologies developed a new detection engine using AI for conventional ECT tubing data. The AI detection model integrated into Magnifiģ is aimed at improving confidence in the analysis of ECT signals and supports the collection of higher quality data.
Read the full article at Eddyfi.com.