Artificial Intelligence-based corrosion sensing and prediction for aircraft applications
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Corrosion causes enormous damage to mechanical structures in many industrial sectors, and the aviation industry is no exception. To extend the lifetime of airframes without compromising safety, it is very important to have a clear picture of the state of corrosion (SoC) of the aircraft. Thus, it is essential to develop methodologies suitable for real time monitoring of the SoC and subsequent reliable notification when a structure has been compromised by corrosion.
Published results so far suggest that the ultrasonic (e.g. Acoustic emission, Guided waves) as well as electrochemical sensors (e.g. Electrochemical noise, impedance spectroscopy) are suitable for monitoring aircraft-relevant corrosion but lack the technological readiness to be applied in commercial aircraft yet. A huge issue in achieving reliable monitoring systems is the correlation between corrosive phenomena and (typically) noisy sensor data.
The AICorrSens project will achieve that by developing a multi-sensor setup for monitoring the SoC based on ultrasonic, electrochemical and environmental sensors coupled with AI algorithms. Training data will be generated performing accelerated corrosion tests with coupons and demonstrator parts equipped with sensors. Using AI for the subsequent data analysis, one can overcome operational noise, and thus, allow today?s corrosion detection methods onboard real-time evaluation of the SoC in terms of detection, localization, typification and quantification.
The ambition of the project is to transform the created continuous stream of data into classifications of the SoC that are intuitively understandable through a human-machine interface, including a qualified corrosion prediction by the AI models generated from test campaigns.
The project results will lead to an increased aircraft safety and reliability and deliver a clear economic benefit for aircraft operators as it allows a switch from regular inspection intervals to State-of-Health-based maintenance.