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This project aims to develop a distributed, grid-based diagnostic maintenance system for aeroengines. The goal is to support engine diagnostics on a global scale by utilizing grid-enabled technologies and demonstrating the process of building a grid-based system. The project also aims to prove the technology and business case, deal with large volumes of engine data, and provide real-time support for diagnostics.
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Grid-based on-line aeroengine diagnostics Jim Austin, University of York
Aims • To build a distributed, Grid based, diagnostic maintenance system • To prove the technology on a Rolls Royce Aeroengine diagnostic maintenance problem • Demonstrate the process of building a Grid based system • To deliver grid-enabled technologies that underpin the application
The Application • The support of engine diagnostics on a global scale.
Engine flight data London Airport Airline office New York Airport Grid Maintenance Centre American data center European data center
Engine data log AURA data search Outline architecture Aircraft Engine Quote Systems Diagnostic operator Maintenance operator Engine data Database of Operational data Operational Report Model-based Interpretation Case-based Reasoning Decision-support
Example use case Perform Extended Analysis <<extend>> Diagnosis / Local Environment Prognosis <<include Decision <<extend>> Support Perform Status / <<extend>> Data Engine Analysis Parameters Pattern Match Modeller Update Provide Domain Update Local Expert Diagnostics Assessment Operation Domain Inform Domain Expert / Expert of Diagnosis Maintenance Undetected Problem Planner Assessment Results Reports Store Result of Diagnosis and Provide Resu lts Operation Statistics Report Maintenance Team
Challenges • Support on-line diagnostics in real time • Deal with the data from 100,000 engines in operation • Prove pattern matching methodology • Prove the business case for the technology
Technologies • AURA: High performance search technology • QUOTE: On-engine diagnostics system • Globus: Grid software • WR Grid: Demonstrator hardware
AURA • High performance ‘search engine’ • Based on neural networks • Develop for distributed operation
QUOTE • On-engine health assessment • Under trials on Trent 500 now • Will identify novelty • Some diagnostics
White Rose Computational Infrastructure Oxford Leeds Cluster Leeds Shared Memory Super Janet White Rose Computational Grid Sheffield Distributed Memory York Shared Memory
Industry Collaborators • DS&S : Rolls Royce data services providers • Rolls Royce : Data and problem • WRCG: Esteem, Sun and Streamline: Demonstrator Grid • Cybula: AURA technology support
Academic team • Austin – project management, data management • Tarassenko, Austin – algorithms for fault identification • Dew, Djemame – system architecture • Fleming, Thompson – decision support • McKay – data modeling
Academic Team • McDermid – Dependability • Wellings – Real time issues Researchers - 15, including... • Tom Jackson - Coordinator • Martyn Fletcher - Software Manager