The aim of the project is to provide new tools for design-risk analysis of complex systems allowing early design decisions to be informed by their reliability impact (and in certain cases, by implication, their safety impact). Additionally, maintenance and fault diagnosis could be based on a deep understanding of the system components and their interactions. Bayesian belief networks (BBNs) will be used to represent entire engineering systems in a probabilistic way (latest generation gas turbines will be used for proof-of-concept) in order to analyse the interactions at both module and component level and perform a design-risk analysis. We shall investigate how uncertainty in probabilities can be represented, inferred with, and used to help designers assess the impact of component and design choices. The techniques will be validated using historical information (including service histories) from existing aeroengines.

Project staff and support

Noel Iradukunda (Postgraduate Associate, Aston University)
Ian Nabney (Academic supervisor, Aston University)
Paul Anuzis (Industrial supervisor, Rolls-Royce)
Melvin Brown (Technology Translator, Industrial Mathematics KTN)

This project is being carried out at Aston University, in conjunction with Rolls-Royce. It is supported by an EPSRC Industrial CASE award, made available through the Knowledge Transfer Network for Industrial Mathematics. Start date: July 2008; duration: 3.5 years.