This is a self-funded PhD position to work with Prof John Erkoyuncu in the Centre for Digital Engineering and Manufacturing.
In the maintenance repair and overhaul (MRO) industry, the maximisation of equipment availability at a minimum cost has become one of the key challenges. Therefore, it is of high importance to be effective with MRO operations. Some of the key challenges emerging are:
– A range of requirements to sustain availability for a variety of complex tasks on a large variety of complex systems;
– Diagnosing the equipment health status proactively with minimal resources.
– Making sure that quality requirements are achieved.
Therefore, a new way of communication and gathering data has to be found, avoiding the current issues encountered in the MRO industry. Digital twins are emerging as an enabler for these. In this age of globalisation and digitalisation, many industries have evolved from a physical space information flow towards a two-way communication between virtual and physical space. The literature is increasingly paying attention to how can a virtual system adjust itself to the constantly changing conditions of the physical space of information that influences the operation dynamics of the MRO requirements. In this process, there are still many challenges with ensuring the digital twin is accurately representative of the physical asset (E.g. plane) and/or processes (e.g. maintenance).
This exciting PhD is aiming to create a new approach whereby the digital twin can 1) realise if there is a deviation from the accurate state of the asset and/or process, 2) understand where the difference originates, and 3) respond by making a change automatically in the digital twin, either by changing the type and amount of information needed, or by adjusting the models to increase the representability of the digital twin. We consider this process to apply concepts from resilience engineering to enhance the responsiveness of the digital twin when there are issues in the accuracy of its representation of the asset / process.
The aim of the PhD is to develop a machine learning based simulation approach to optimise the accuracy of the digital twin representativeness of an asset/process, bearing in mind the ability to response to the inaccuracy, costs, resources, and timeliness. The machine learning will rely on data from past inaccuracies experienced in the digital twin, data mining to explore data across different projects, dynamic root cause analysis, and both tangible and expert opinion based data.
The PhD will rely on case studies from the aerospace, defence, and wind sectors. The project will target to enhance the trust in the digital twin by its operators. Accordingly, there will be some research required to understand the implication of introducing resilience to the digital twin on the operators. This PhD will bring together a number of research themes in the fields of digital twins, data mining, AI and machine learning, optimisation and maintenance.
Engineering & Technology, Manufacturing and Production Engineering
3 years | Full time
Not defined yet
Develop a data structure to support the digital twin needs
Develop an approach to self-learn if there is a deviation from accurate asset/process representation in the digital twin
Develop an probabilistic risk based approach to identify where the deviation in the accuracy originates, and to automatically understand its data and model sources
Develop an machine learning based approach to self-adapt the digital twin to increase its accuracy of representativeness.
Develop and validate a simulation toolkit with machine learning features to optimise the accuracy of the digital twin
Apply use cases to evaluate the trust in the digital twin by introducing the resilience features.
At Cranfield, the candidate will be based at the Centre for Digital Engineering and Manufacturing which hosts cutting-edge digital engineering facilities. The student will have access to high-end computers for simulating the complex nature of maintenance. The candidate will work on his/her research individually with supervisors and collaborates with other researchers in the field at the Centre.