Automated fatigue crack detection for structural health monitoring of metallic aerospace structures

Structural health monitoring (SHM) offers the ability to monitor structures, in real time, throughout their service lives to ensure they are safe for operation. Such technology has the potential to provide significant savings in maintenance and repair costs whilst increasing safety. For this reason there is currently a significant technology pull through for SHM techniques by the aerospace, energy, transport and infrastructure sectors.

Acoustic emission is a passive SHM technique that offers the ability to globally monitor large structures in real-time by detecting small amounts of energy that are released when damage grows in a structure. However, like many SHM techniques acoustic emission requires operator interpretation of data and can be less reliable in complex structures, which is seen as the greatest barrier to industrial implementation.

This project presents an excellent opportunity to work with staff from the Schools of Engineering and Mathematics and industry partners MBDA. The aim is to address two key challenges: 1) the accurate location of AE sources within a complex 3D geometry and 2) the automated identification of AE signals from crack growth in the presence of extraneous noise. The development of data processing algorithms will be the main focus of the project and the applicant will develop skills in coding (Matlab), signal processing and statistics. Techniques for clustering and classification of data sets will be explored, whilst developing a strong understanding of the science behind the acoustic emission technique. A requirement to generate data and validate developed techniques brings an experimental aspect to the project where the applicant will gain experience of acoustic emission equipment and fatigue testing.

Candidates should hold or expect to gain a first class degree or a good 2.1 and/or an appropriate Master’s level qualification (or their equivalent).

Applicants whose first language is not English will be required to demonstrate proficiency in the English language (IELTS 6.5 or equivalent)