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Advanced Quantification of Uncertainty for Maintenance in Complex Engineering Systems

  • Full or part time
  • Application Deadline
    Monday, September 26, 2016
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Start Date: ASAP
Eligibility: UK
Duration of award: 3 years
Ref: CRAN1137
Dr. John Ahmet Erkoyuncu – Lecturer in Service Simulation and Visualisation
Professor Rajkumar Roy – Director of Manufacturing

Sponsored by Dstl and EPSRC, this studentship will provide a bursary of up to £16,000 - £19,000 p.a. (tax free) plus fees* for three years

This exciting PhD project aims to develop uncertainty quantification approaches for equipment maintenance in service delivery. During the in-service phase of complex engineering systems (CES) in the defence context, the equipment is expected to carry out the intended functions whilst maintaining reliability. There is an increasing trend to develop performance based targets such as equipment availability to manage the in-service phase of CES. This promotes further challenges with how to reduce cost, and manage performance over the life cycle due to the level of uncertainties.

The dynamic nature and complexity of the extended enterprise (customer, and suppliers) drives an inherently high level of uncertainty, and as such it is extremely challenging to predict the equipment cost and availability. The scientific assumption to be tested in this PhD will focus on how technical/engineering uncertainties and the wider enterprise related uncertainties associated to the supply chain, logistics solutions and requirements definition can be quantified in CES. This will aim to offer techniques to quantify uncertainty in a scientific manner for various scenarios where the data is maybe incomplete, inconsistent, inaccessible, and relies on expert opinion. Overall, there is a need to estimate the resources required for maintenance to meet availability and affordability and understand the logistic requirements. However, uncertainty prevents the ability to estimate the availability and cost of maintaining equipment and the wider enterprise related uncertainties with confidence. Thus, there is a need for uncertainty quantification approaches, which sets the background to this PhD project, which is co-sponsored by Dstl and the UK MoD.

Uncertainty is the stochastic behaviour of any physical phenomenon that causes the indefiniteness of outcomes, which means the expected and actual outcomes are typically not the same. The technical uncertainties for maintenance may include: degradation, no-fault found, obsolescence, and failure. On the other hand, the enterprise related uncertainties may include: enterprise resilience, requirements, supply chain integration, and organisational re-structuring. In this PhD there are multiple challenges of interest:

• How can we aggregate uncertainties across multiple elements that may be represented through different probability distributions? The current practice simply considers a summation of the best and worst case scenarios to define the boundaries for likely outcomes. Though, further guidance on how uncertainty can be aggregated is required.
• Expert opinion driven uncertainty quantification; where there is a lack of standardised way to quantify uncertainty and the experiences of individuals play a key role in determining the potential impact on cost and availability.

This project will run in collaboration with Dstl, which will offer opportunities to test the solution in the military context including equipment in the land, air and marine domains.

The student will be based at the Cranfield University in the Through-Life Engineering Services Centre which is a part of the Manufacturing Department. The student will use the Operations Excellence Institute for simulation studies and the latest computing facilities.

As a part of working in this industrially sponsored project, the student will work in close collaboration with Dstl, Ministry of Defence UK and the wider Defence industry including organisations such as BAE Systems, Rolls-Royce and BABCOCK International. He/she will need to present their research findings regularly to the project team.

Entry requirements:
Applicants should have a first or second class UK honours degree or equivalent in a related discipline, such as mathematics, or engineering. The ideal candidate should have some understanding in the area of simulation, numerical modelling and industrial service delivery. The candidate should be self-motivated, have good communication skills for regular interaction with other stakeholders, with an interest for industrial research.

Funding Notes

* Applicants must be a UK national. We require that applicants are under no restrictions regarding how long they can stay in the UK i.e. have no visa restrictions or applicant has “settled status” and has been “ordinarily resident” in the UK for 3 years prior to start of studies and has not been residing in the UK wholly or mainly for the purpose of full-time education. (This does not apply to UK or EU nationals). Due to funding restrictions all EU nationals are eligible to receive a fees-only award if they do not have “settled status” in the UK.

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