Eddy current non-destructive testing for inspection of reformer tubes applying machine learning
What is it about?
This study focused on monitoring the aging process of high-performance austenitic stainless steel when exposed to harsh environments like those found in steam reforming furnaces. Specifically, the researchers aimed to classify different aging states of the steel based on temperature and microstructural changes, which are important for assessing its remaining lifespan. They used a portable Eddy Current inspection system along with machine learning techniques to achieve real-time characterization of the aging states. The study involved analyzing a full-sized tube to emulate in situ inspections.
Why is it important?
This research is particularly crucial for industries in the oil and gas sector, where stainless steel equipment is extensively used, primarily in steam reforming furnaces. By accurately monitoring the aging of stainless steel components in these furnaces, the oil and gas industry can prevent potential failures, leaks, or accidents that could have significant environmental and economic consequences. This approach helps ensure the reliability and safety of operations while optimizing maintenance schedules and resource allocation.