Developing digital twins of through-life degradation for high value systems PhD
Cranfield University and BABCOCK International are seeking a top class candidate to undertake research leading to PhD awarded. This innovative research aims to deliver a smart digital platform capable of capturing the health of a high-value system digitally and provide a cognitive decision system through technologies such as human-in-the-loop (HITL).
This opportunity also provides a substantial tax-free stipend (between £18k-£20k) equivalent to many graduate jobs.
Cranfield University and BABCOCK International are seeking a top class candidate to undertake research leading to the award of PhD in Manufacturing.
For more than a century, Babcock International has been trusted to deliver bespoke, highly-skilled engineering support. Underpinned by a deep understanding of technology integration, infrastructure management, and specialist training, BABCOCK help customers around the world to improve the capability, reliability and availability of their most critical assets within the key market sectors of Marine, Land, Aviation, and Nuclear.
The PhD is seeking to develop the concept of a digital twin for a series of subsystems (e.g. engines) to understand how subsystem degradation within a complex system (e.g. ship) can be modelled to understand the degradation performance of the overall system. Digital twins are an emerging technology that enables to replicate the physical asset and processes. The digital twin will aim to capture and store data in the digital form so that we can have a networked interconnection for planning the through-life requirements of the complex system. This PhD will consider the complexities experienced within the system of system context where degradation can have knock-on effects. The PhD will develop artificial intelligence-driven approaches to predict how degradation evolves over time and optimise proactive behaviour to achieve complex system-level targets such as asset availability and minimised cost.
The digital twin to be developed in this PhD aims to deliver “Availability” of a major asset against a customer requirement (Demand). This will contribute to matching the supply of the different contributing service resources that will assist in making an asset available for the customer to undertake their operations. The PhD will develop degradation optimisation methods embedded in the digital twin to enable the planning process.
As part of the research, the candidate will be exposed to a variety of maintenance activities that are taken up with the high-value engineering sector with a focus on how degradations can be captured and represented in the human cognition based digital system. The research will be able to exploit multiple degradation scenarios and will use real-working assets to capture degradation data and digitalise both the data and decision systems to train the digital twin. Technologies such as non-destructive testing (Thermography, X-Ray, Ultrasound), digital service engineering toolsets (Augmented Reality, Virtual Reality, Modelling & Simulation) will be used to evaluate the real-time remaining useful life predictions with potential asset failure trajectories through advanced visualisation techniques.
The aim is to have a true digital twin of a physical asset capable of supporting additional performance attributes or value propositions for the Sponsor such as:
• Improved reliability (how often the major asset breaks down, a failure rate)
• Improved responsiveness (How long it takes to fix a broken major asset)
• Improved agility (How quickly the organisation can change its course of actions or service offer)
• Reduced costs (How much expenditure is required to pay for operating the service resources)
Type of opportunity
There will be relevant visits to various sites of BABCOCK International throughout the PhD to develop and demonstrate the research.
About the sponsor
Sponsored by BABCOCK International, this studentship will provide a bursary between £18,000-£20,000 (tax free) plus fees* for three years. The funding will also cover a travel allowance and consumables.
Candidates should have a minimum of an upper second (2.1) honours degree (or equivalent) preferably in 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. Experience in programming Matlab / C C++ / python / R and relevant background in maintenance dealing with non-destructive testing is desirable.
How to apply
To find out more, head to http://www.cranfield.ac.uk/research/phd/developing-digital-twins-of-throughlife-degradation-for-high-value-systems
If you are eligible to apply for this studentship, please complete the online application form by clicking on ’Visit Website’.
For further information please contact:
Name: Dr. John Erkoyuncu
Email: [email protected]
T: (0)1234 75 4717