12 April 2013

Industrial CASE PhD Studentship in collaboration with Rolls-Royce plc.

Applications are now invited for the Industrial CASE PhD Studentship: Prognosis for Gas Turbine Engine Health Management in collaboration with Rolls-Royce plc.

Essential details

Funding: Full EPSRC studentship for 3.5 years (currently ~£67,443), includes six month Industrial Placement.

Supervision: Professor R. F. Harrison and Professor V. Kadirkamanathan

Duration: from 09/2013 to 08/2016

Application Deadline: 31 July 2013

Eligibility: Full grant requires UK residency status, please see:  http://www.epsrc.ac.uk/skills/students/coll/icase/Pages/faqs.aspx

Enquiries and Applications: a.r.mills@sheffield.ac.uk, please see requirements below.

Description

The Department of Automatic Control & Systems Engineering is the largest department in Europe dedicated to the study of control and systems engineering and has a strong track record corroborated both by UK Government Research Assessment results and by the strength of its industrial collaborations.  The Department has worked at the forefront of equipment health monitoring within the Aerospace industry for many years.  The advances made through these research activities have been applied to real industrial systems through the Rolls-Royce-sponsored University Technology Centre in Control and Systems Engineering, hosted by the Department for 20 years.

The research programme will focus on developing innovative technology for gas turbine power systems for civil and marine applications. These are subject to operational degradation that, over time, will reduce their performance. For effective fleet management, the ability to predict this degradation (prognosis) is seen as an increasingly important part of modern health monitoring.  Prognostics enable forward predictions of e.g. time-to-failure, thus offering a route to increase time-in-service and reduced fleet-wide operational costs.

To achieve the challenging performance demands of Rolls-Royce products, novel ideas are continuing to be developed to advance the state-of-the-art.  The power systems of the future will be adaptive to their condition and have the ability to perform on-board interactive and progressive diagnosis with reconfigurable monitoring systems.  These future complex systems will need advanced health monitoring that can predict system failures emerging out of the interaction of seemingly healthy components.  Monitoring will require use of heterogeneous and multi-level system data (component, asset, fleet, environment, etc.) to model the system over its lifetime of environmental interaction.
The expected outcome of this PhD study will be to produce a step change in the way prognosis is currently performed. The PhD will use industrial data as a basis for creating state-of-the-art predictive models and develop techniques for the proper aggregation of widely differing data-types and a variety of information sources. Techniques from machine learning, such as Bayesian statistics, system identification, time series analysis and system dynamics will be employed to provide prognostic and diagnostic tools and methodological procedures. A minimum of six months worth of placements within Rolls-Royce will provide opportunities to engage with engineers dealing first-hand with health management and will enable you to understand and, ultimately, influence the management of complex systems degradation.

You should have an excellent first degree in a relevant branch of engineering, mathematics, statistics or physics, and be strongly numerate with an aptitude for programming. You will be an excellent communicator in both written and spoken English. An application should be by email and include CV containing the names and addresses of two referees, along with a 150-word (maximum) description of your relevant experience for this research programme.