AI and machine learning is opening up many new opportunities in the context of maintenance, particularly to allow assets to have autonomy. This exciting PhD is aiming to develop a digital twin infrastructure to facilitate self-repair type features in aerospace related components. We are aiming to create a process whereby data will be automatically collected and distributed for data analytics purposes using machine-learning features. This will have the aim of creating an interface for autonomy in maintenance of aerospace products. Read less
This exciting PhD focuses on cutting-edge aerospace maintenance technology.
Autonomy is becoming a focal point for aerospace research. Whilst this was traditionally achieved by modelling self-engineering behaviours at the component-level, efforts need to focus on sub-system and system-levels through advancements in artificial intelligence. Exploiting its benefits requires some innovative thinking to integrate overarching concepts from sensing, digitisation, state estimation, optimisation, control and systems engineering. With recent requirements in aeronautic and astronautic systems, there has been a growing interest in adapting these concepts to achieve autonomy during concept design, operational service, the automation of predictive maintenance scheduling directly from operational data and for in-built repair at the systems-level. Through a digital condition assessment, on the actual system's components, the project will resort to innovative technology to trigger self-repair; to extend properties life-cycle and lifespan, and improve efficiency to maintain high performance.
The aim of the PhD is to investigate the application of autonomous maintenance. It focuses on the following research questions:
• Can a digital twin introduce autonomy for self-maintenance?
• What are the requirements for such a digital twin driven framework?
• How can this concept be realised for aircraft engine components?
The project will need to encompass lots of concepts and technologies; Internet of Things (IoT), big data analytics and machine learning. Consideration should be given to these concepts, and the scalability for future technology. The project will consider the information or data that needs to be collected within the context of maintenance e.g. what data, where from, who for, what for?
The technology will facilitate Prognostics and System Health Management (PHM) in different areas such as a) AI-based diagnostics and prognostics, c) Autonomous decision-making, and d) Self-healing or repair. Each of these areas has quite distinct skills set and behaviours associated with them.
1. Determine in-situ implementation complexity requirements;
2. Determine the required levels of autonomy for PHM of aircraft engine components;
3. Addressing the technological barriers, i.e., lack of computation, knowledge representation, network performance, interfacing and communication;
4. Validation of the concept in the aerospace environment.
At Cranfield, the candidate will be based at the Centre for Digital Engineering and Manufacturing, which hosts cutting-edge simulation and visualisation facilities. The student will have access to high-end computers for simulating the complex nature of maintenance. There will be relevant visits to various organisations throughout the PhD to develop and demonstrate the research.
Candidates should have a minimum of an upper second (2.1) honours degree (or equivalent) preferably in Computer Science/ Mechanical Engineering / Industrial Engineering / Mathematics / Operations Research but candidates in other degrees related to Engineering or related quantitative fields would be considered. Candidates with an MSc degree in these disciplines will be desirable.
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