Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in industry and the academic community.
PO200: Early detection and monitoring of pitting corrosion in low-carbon steel using pulse-echo ultrasound.
Marina Perez Diego, Predoctoral Researcher in Advanced Signal Processing for Structural Monitoring, CEIT
Abstract
Pitting corrosion is a localized electrochemical process that produces small cavities in a metal surface when the protective oxide film or coating is disrupted, often by aggressive ions such as chlorides. It is particularly critical in humid, acidic, or saline environments, as encountered in offshore wind turbines. Detection is challenging because pits initiate in extremely small areas and often remain unnoticed until they cause severe damage or structural failure. For carbon steels, unlike stainless steel or aluminum, the mechanisms of pitting and their effects on structural integrity are not fully understood, and no reliable real-time monitoring methods are available. This work explores the potential of ultrasound pulse-echo technology for evaluating pitting corrosion in low-carbon steel and developing a monitoring methodology. The technique is appealing due to its non-destructive nature and simplicity, using a single-element transducer to emit and receive high-frequency pulses. The study used an ad-hoc Ultrasound Testbed to perform measurements on samples with artificially created pits of varying depths and shapes. Raw signals were processed in MATLAB to extract features indicative of pit presence and quantify their characteristics. It was observed that transducer selection plays a critical role, with diameter influencing the relative power reflected from the pit and central frequency determining axial resolution. Additionally, both pit depth and geometry significantly influence how pits are manifested in the measured ultrasound signals. Experimental results were correlated with simulations using the k-Wave finite-difference time-domain (FDTD) tool to replicate measurements. This could provide a framework for quantifying pitting in terms of pit shape and depth and holds potential for generating synthetic signals to support predictive modeling. To extend this approach, autonomous sensor nodes were designed for attachment to pitted and non-pitted samples and are planned for deployment in HarshLab (https://harshlab.eu/en/) to monitor pit evolution and collect data for predictive models.
No recording available for this poster.
