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

The project

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.

This project is supported by Airbus Operations UK Ltd, through technical support, a significant top-up to the EPSRC tax-free stipend, and by hosting a placement at Airbus as well as providing access to the test facilities at AIRTeC.

Self-motivated individuals are sought with relevant mathematical and engineering expertise, along with good communication skills and a willingness to work on an industrially focused PhD. An interest in, or previous exposure to, experimental structural testing, including advanced measurement methods such as acoustic emission monitoring is desirable. This project will require the PhD candidate to develop skills in data analytics, big data, machine learning, and artificial intelligence applied to signal processing. Experience of using MATLAB and finite element analysis software is also desirable.

We welcome all applications irrespective of social and cultural backgrounds and encourage prospective applicants to reach out for an informal discussion with the main supervisor.

Candidate requirements

Applicants must hold/achieve a minimum of a pass at master’s degree level (or international equivalent) in a science, mathematics or engineering discipline. Applicants without a master’s qualification may be considered on an exceptional basis, provided they hold a first-class undergraduate degree. Please note, acceptance will also depend on evidence of readiness to pursue a research degree.

If English is not your first language, you need to meet this profile level: Profile E

Further information about English language requirements and profile levels.

How to apply

Prior to submitting an online application, you will need to contact the project supervisor to discuss.

Online applications are made at http://www.bris.ac.uk/pg-howtoapply. Please select Aerospace Engineering on the Programme Choice page. You will be prompted to enter details of the studentship in the Funding and Research Details sections of the form.

Contacts:

For questions about the research topic, please contact Dr Neha Chandarana [email protected] or Prof Paul Wilcox [email protected].

For questions about eligibility and the application process please contact Engineering Postgraduate Research Admissions [email protected]