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

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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 (AE) is a passive SHM technique that is able 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 problem is made all the more challenging by the presence of uncertainty which results from measurement noise, lack of system knowledge, variability in operating conditions, etc. The applicant will develop excellent skills in signal processing, multi-variate statistics, novelty detection and uncertainty analysis. The aim of the work is to develop data processing methodologies that allow the automated detection of fatigue cracking in aluminium hangers. This will include techniques for accurate location of damage, the separation of extraneous noise from fracture signals, novelty detection and feature extraction. The developed techniques will be supported and validated by an extensive experimental fatigue testing programme, where the applicant will gain expertise in acoustic emission monitoring and experimental testing. The developed skills will also be widely applicable to the monitoring of a range of structures outside the initial aerospace focus. Supporting Documentation.