Artificial Intelligence-based corrosion sensing and prediction for aircraft applications

Airframes, which are primarily constructed of aluminium alloys are susceptible to several types of localized corrosion, which frequently occurs without any outward signs of damage. The corrosion “hotspots” are sometimes difficult to access and inspect. Several sensor nodes distributed at various corrosion hotspots throughout an airframe could provide a view of the overall structural health of the aircraft as well as an assessment of the structural integrity within individual microclimates. This sensors create a continuous stream of data which must be collected, processed and interpreted to deliver useful results to the end-user who has mainly the interest to be informed about the current status of the structure in terms of corrosive degradation, the place of corrosive degradation on the structure and if and when part may fail. Therefore, this project will apply artificial intelligence methods to fuse the collected sensor data, label them according to classical corrosion assessment methods (e.g. salt spray chamber and subsequent optical assessment, metallography) with the goal to build a robust model which can deliver the aircraft user suitable variables which asses the state of the structural part.