Grid-based on-line aeroengine diagnostics Jim Austin, University of York The DAME pilot project

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Grid-based on-line
aeroengine diagnostics
The DAME pilot project
Jim Austin, University of York
The Problem
• Information is being gathered at growing
rates at an international level
• This information needs to be fully utilized
• Technology is needed to integrate and
exploit the data
• Grid is a part of the answer to this
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
Diagnostics centre
Maintenance Centre
American data center
European data center
5
Aircraft
Engine
Outline architecture
Quote
Systems
Diagnostic
operator
Engine data
Engine data
log
AURA
data search
Operational
Report
Model-based
Interpretation
Maintenance
operator
Database of
Operational
data
Case-based
Reasoning
Decisionsupport
6
Example use case diagram
GRID Environment
Local Environment
<<extend>>
Data
Engine
Perform
Extended
Analysis /
Modelling
Diagnosis /
Prognosis
Status /
Parameters
Perform
Analysis
Update
Operation
Provide Domain
Expert
Assessment
Update Local
Diagnostics
Diagnosis
Assessment
Inform Domain
Expert of
Undetected Problem
Results
Reports
Information
Results
Maintenance
Team
Domain
Expert /
Maintenance
Planner
Store Result of
Diagnosis and
Operation
Provide
Statistics
Report
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
12
White Rose
Computational Infrastructure
Oxford
Super
Janet
Leeds Cluster
Leeds Shared
Memory
White Rose Computational Grid
Sheffield
Distributed Memory
York Shared
Memory
13
Our developing architecture
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
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
Academic Team
• McDermid – Dependability
• Wellings – Real time issues
Researchers - 15, including...
• Tom Jackson - Coordinator
• Martyn Fletcher - Software Manager
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End
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