Bayesian inference approaches for the detection and characterization of hidden pitting corrosion

In this work, we consider a thermographic inspection setup for detection and characterization of corrosion in a steel structure. Solid products of corrosion, if present, interact with the flow of heat within a domain, so that a structure's thermal response to a known energy input can provide information about internal damage. We focus our study on pitting corrosion, which complicates the detection and characterization tasks because the damage can be both small and hidden on inaccessible areas of the structure. Nevertheless, pitting corrosion poses a threat to structural components, so early detection is important. We develop a procedure based on Bayesian mathematical analysis to achieve rigorous inference over locations and geometric forms of hidden corrosion pits based on their subtle influences within noisy temperature measurements made on observable portions of the structure. A strategy is developed for optimizing a thermographic framework for the purpose of maximizing this response. Numerical experiments are performed to demonstrate our proposed corrosion detection and characterization procedure. It is found that reliable inference can be done on corrosion pits millimeters deep in the rear side of a steel panel using observations on the front side only. The Bayesian inference method is also shown to be effective in more complex structural geometries. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This work focuses on a thermographic inspection setup for detecting and characterizing corrosion in steel structures, specifically pitting corrosion. The study utilizes Bayesian mathematical analysis for rigorous inference on the locations and geometric forms of hidden corrosion pits. Numerical experiments demonstrate reliable inference on corrosion pits and the effectiveness of the Bayesian inference method in complex structural geometries.