1. Jim Austin, University of York Grid-based on-line aeroengine diagnostics

2. 2 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

3. 3 The Application The support of engine diagnostics on a global scale.

4. 4 Engine flight data Airline office Maintenance Centre European data center London Airport New York Airport American data center Grid

5. 5 Quote Systems Model-based Interpretation Case-based Reasoning Decision- support Database of Operational data Operational Report Engine data Aircraft Engine Engine data log AURA data search Diagnostic operator Maintenance operator Outline architecture

6. 6 Update Local Diagnostics Perform Extended Analysis Pattern Match Modeller Store Result of Diagnosis and Operation Provide Statistics Report Engine Maintenance Team Domain Expert / Maintenance Planner Operation Diagnosis Assessment Update Results Reports Status / Parameters Perform Analysis Data Results > Local Environment Diagnosis / Prognosis > Provide Domain Expert Assessment Inform Domain Expert of Undetected Problem > Decision Support <<include Example use case

7. 7 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

8. 8 Technologies AURA: High performance search technology QUOTE: On-engine diagnostics system Globus: Grid software WR Grid: Demonstrator hardware

9. 9 AURA High performance ‘search engine’ Based on neural networks Develop for distributed operation

10. 10 QUOTE On-engine health assessment Under trials on Trent 500 now Will identify novelty Some diagnostics

11. 11

12. 12 White Rose Computational Infrastructure Leeds Cluster Leeds Shared Memory White Rose Computational Grid York Shared Memory Sheffield Distributed Memory Super Janet Oxford

13. 13 Our developing architecture

14. 14 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

15. 15 Academic team Austin – project management, data management Tarassenko, Austin – algorithms for fault identification Dew, Djemame – system architecture Fleming, Thompson – decision support McKay – data modeling

16. 16 Academic Team McDermid – Dependability Wellings – Real time issues Researchers - 15, including... Tom Jackson - Coordinator Martyn Fletcher - Software Manager

17. 17 End