BROADEN
Business Resource Optimisation for Aftermarket
and Design on Engineering Networks
Prof. Jim Austin
Computer Science, University of York UK
CEO Cybula Ltd.
Outline
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Bit of History
What was the data and processing challenge
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Data Management
Data analysis
Middleware architecture
The industry problem: Aerospace
The science problem: Neuroscience
Scaling to other industries
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History
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Started with DAME
Distributed Aircraft Maintenance
Environment
Worked with Rolls-Royce to develop ideas
and learn about the Grid.
Completed in 2003
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Partners
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Rolls-Royce plc.
EDS Defence Ltd.
Lost Wax Media Ltd.
Concurrent Thinking Ltd.
Oxford BioSignals Ltd.
Cybula Ltd.
Computing Laboratory, University of Oxford
Department of Computer Science, University of York
Informatics Research Institute, School of Computing,
University of Leeds
Department of Automatic Control and Systems Engineering,
University of Sheffield
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History
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BROADEN started at the end of DAME
Ran for 3 years, from Jan 2005. DTI funded
Four objectives:
(1) large scale and CFD simulation and visualisation
(2) very large scale agent-based modelling of aftermarket
processes incorporating logistics and the supply chain
(3) integrated diagnostic and prognostic tools for health
monitoring across development test, production pass-off
and in-service aero engines
(4) the formulation of a strategy to transfer proven Grid technology
to production LANs
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History
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BROADEN
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Took DAME into Rolls Royce
Focussed on deploying DAME technology
Built a Rolls Royce Grid
Tested technology on test bed systems
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The generic challenges
Challenge
Data
Asset or
System
Data Store
Knowledge
• Massive data collection happening everywhere
• Focus of TSB last call
• BUT Need to get knowledge from the data
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Challenge
Europe
China
US
One
Data
Warehouse
Japan
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Rolls-Royce
Engine flight data
Engine flight data
London Airport
New York Airport
Airline office
GRID
Diagnostics
Centre
Maintenance
Centre
US data centre
European data centre
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Challenge
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Single data store is a processing
bottleneck
Data changes too fast to communicate it
and keep up to date
Networks too slow to transfer the data
The system has a central point of failure
Large processing needed on centralised
store
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Data & processing
management
Local nodes
PME
PMC
PME
PMC
PME
PMC
PMC - Pattern Match Controller architecture :
manages processing
PME - Pattern Match Engine : Manages pattern match
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PMC and PME
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Do not move the data - move the processing
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That’s much lower effort
More reliability
Scalable
Networks need not be high bandwidth
Leverages available local computation
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Data analysis
The Signal Data Explorer
SDE
Data feed
Asset
Data silos
!
Event detector
Basic SDE data process
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SDE
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Allows a user to:
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Set up triggers on multiple, complex, real time
data feeds.
Find examples of events that are seen but
unknown.
Visualise and ‘explore’ the data
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SDE
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Signal Search
and analysis
Finds
examples like
previous
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SDE
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SDE
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SDE
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SDE
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SDE
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SDE principles
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Focussed on
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High performance search using neural
networks and other methods
Very fast visualisation
Working with the user to solve the problem
through ‘helpful’ tool sets, such as CBR etc.
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Link to other tools
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Leeds developed CBR under DAME and
BROADEN
Quick toolset developed by Oxford/Oxford Biosignals
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(most valuable patent in RR last year!)
Re-deployed in other areas of RR and
elsewhere
SDE-CBR essential for full operation
CBR-Matlab and other services in the pipeline
within CARMEN
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Portal
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Enterprise system
Enterprise
system
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Looked at service bus’s for integrating
diverse services (York)
Looked at portal designs that were flexible
(now applied in CARMEN)
Workflow manager, a resource broker and
Service Level Agreement (SLA) manager
(Leeds)
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WorkBench
Workflow
Manager
(or PXE)
Resource
Broker
Other
Services
This diagram represents an architecture with a distributed
QUICK Feature Provider and XTO sharing a common Distributed
Controller with the Pattern Match Service (see notes)
Enterprise Service Bus (ESB)
CBR
Viewer
CBR
Engine
PCT
Analysis
Visualiser
PCT
Analysis
Visualiser
CBR KnowledgeBase
PCT
Viewer
SDE
(Limited
IRRSIS)
QUICK
DataViewer
GRID
Factory
Distributed Node 0
?
Distributed
Controller
PMS
Service
(XTO)
Distributed
Node 1
Distributed
Node 2
QUICK
Feature
Provider
XTO
Centralised
Remote
Storage Request Broker (SRB)
Master node 1 (with MCAT)
Database for other data
(e.g. RDF, extractions, etc.)
New Engine
Data
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PMS
Search
Database
Database for
Meta Data
Zmod
Database
Data
Orchestrator
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Deployment in Rolls Royce
Progress
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Working with a large company is a long
slow process
Tool is on the desktop within Rolls Royce
RR Grid used for CFD etc.
Currently looking at blade tip flutter
Looking at new deployments with Cybula.
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Science
Neuroscience: CARMEN
Science
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BROADEN tackled Industry, CARMEN is
aimed at Science.
Data is often not shared from experiments
Individuals duplicate many expensive
experiments
Should share both data and methods
Distributed data and service repositories
needed to support this
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CARMEN
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Tackles the scientific application of SDE
Builds on the York’s teams 6 years of
experience in building these applications.
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Tom Jackson, Martyn Fletcher, Bojian Liang
and Mark Jessop
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Freeflow
Use of intelligence to
improve traffic flow
Freeflow
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A TSB/DfT/EPSRC project, £4.5M
Looking at using the SDE tool to look at
traffic data from road networks
Initial trails in York (Hull Road), London
(Hyde Park Corner) and Kent.
York trial later this year.
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Objectives
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To use SDE to allow users to explore
traffic data
Flows (from loops), phases of traffic lights
etc.
User can then set up event triggers, which
are down loaded to the Pattern Match
Engine for action
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Scaling to other sectors
Other sectors though Cybula
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Stand alone SDE tool (out of the box).
Medical
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Rail industry
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Pipeline monitoring – for leaks
Power Generation
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Track and carriage monitoring
Oil and Gas
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Visualisation of MEG data (York Neuro-imaging)
Analysis of renal data
Monitoring of generation equipment
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Thanks
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DAME, BROADEN, Freeflow and
CARMEN teams
Support of EPSRC, DfT, DTI and industrial
collaborators
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