1 / 17

Jim Austin, University of York

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

bette
Download Presentation

Jim Austin, University of York

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

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

  5. 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

  6. 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

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

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

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

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

  12. Our developing architecture

  13. 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

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

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

  16. End

More Related