"Development of a Novel Structural Health Monitoring system using Machine Learning Approaches"

"This PhD project will involve working with the Structures Test Department at Airbus, which is interested in the development of novel structural health monitoring (SHM)

approaches for composite and metallic aircraft structures during static and fatigue testing. In particular, we are interested in the implementation of acoustic and ultrasonic methods to conduct passive and active SHM and to use machine learning approaches for analysis of the acquired data. While modern passive piezoelectric sensor-based acoustic emission (AE) monitoring provides an opportunity for localisation and intensity quantification of damage sustained during structural testing, current methods are limited when applied to continuous monitoring of - often large - data streams. This

project will look to develop a practical approach to utilise raw AE data in its continuous stream format for damage localisation and classification. Initial tests on small-scale

structures will pave the way for application on larger length scales and inform the development of machine learning-based data processing algorithms. This project has the potential to significantly improve testing efficiency, enable predictive maintenance, and contribute to enhancing the structural design process."