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Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Alex Shenfield
Modelling, Optimisation and Modelling, Optimisation and
Decision Support Decision Support
Using the GridUsing the Grid
Rolls-Royce University Technology Centre in Control & Systems EngineeringDepartment of Automatic Control & Systems Engineering
The University of Sheffield, UK.
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Overview of Presentation
• Introduction :– UK e-Science DAME project– Motivation for DAME
• DAME Grid-Based Diagnostic System– Case Based Reasoning– Model Based Fault Detection and Isolation Approaches– Genetic Algorithms for Many-Objective Optimisation– Use Case
• Conclusions
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Introduction to DAME
• £3.2M UK e-Science Pilot Project
• Develop, and promote understanding of :– Grid middleware and
application/services layer integration
– Real-time issues in Grid Computing
– Dependability Issues
• Provide a “Proof of Concept” demonstrator for the Rolls-Royce Engine Diagnostic problem
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Project Partners
• Four UK Universities :– University of York
• Computer Science Department– University of Sheffield
• Automatic Control and Systems Engineering Department
– University of Leeds• School of Computing• School of Mechanical Engineering
– University of Oxford• Engineering Science Department
• Industrial Partners :– Rolls-Royce Aeroengines– Data Systems and Solutions– Cybula Ltd.
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Motivation for DAME
• Increasing amounts of engine data being collected– New engine monitoring units record up
to 1 Gbyte of data per flight– Rolls-Royce currently has over 50,000
engines in service with total operations of around 10M flying hours per month
– In the future, terabytes of data will be transmitted every day for analysis
• Key Objectives– Reduce delays– Reduce cost of ownership for the
aircraft
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
“Reasoning by remembering, reasoning is remembered.”
Case-Based Reasoning
• CBR is a mature, low-risk subfield of AI
• Primary knowledge source– A memory of stored cases
recording specific prior episodes– Not generalised rules
• New solutions generated by adapting relevant cases from memory to suit new situations
Retrieve
Propose Solution
Adapt Justify
Criticize
Evaluate
Store
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
CBR Maintenance Advisor
• Integrates fault information and knowledge gained from the fault diagnosis process
• Emulate the diagnostic skill of an experienced maintenance engineer
• Advises maintenance personnel on appropriate maintenance action
• Deployed as a Grid Service
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
CBR Engine Architecture
SQL Database
Database Manager
CBR Engine(API)
Service Interface
Grid/Web Service Client
(Web Browser)
• Description of situation
• Description of problem
in that situation
• Description of how
problem was addressed
• Results or outcome of
addressing the problem
in that way
“CASE”
• Description of situation
• Description of problem
in that situation
• Description of how
problem was addressed
• Results or outcome of
addressing the problem
in that way
“CASE”
• Description of situation
• Description of problem
in that situation
• Description of how
problem was addressed
• Results or outcome of
addressing the problem
in that way
“CASE”
• Description of situation
• Description of problem
in that situation
• Description of how
problem was addressed
• Results or outcome of
addressing the problem
in that way
“CASE”
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
CBR Engine Architecture
SQL Database
Database Manager
CBR Engine(API)
Service Interface
Grid/Web Service Client
(Web Browser)
• Interface between application and data
• Reconfigurable
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
CBR Engine Architecture
SQL Database
Database Manager
CBR Engine(API)
Service Interface
Grid/Web Service Client
(Web Browser)
• Contains CBR matching and ranking algorithms
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
CBR Engine Architecture
SQL Database
Database Manager
CBR Engine(API)
Service Interface
Grid/Web Service Client
(Web Browser)
• Processes calls to the CBR service
• Returns results from the CBR service
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
CBR Engine Architecture
SQL Database
Database Manager
CBR Engine(API)
Service Interface
Grid/Web Service Client
(Web Browser)
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Model Based FDI
• Data from the real engine is compared against data from the ideal model
• The residuals then need to be analysed to work out the state of the engine
• This can be used to track changes in engine parameters which may indicate impending faults
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Engine Modelling and Simulation Service
• Based on the Rolls-Royce Trent 500 engine model
• Deployed as a service on the Grid
• Accessible via web browser on the internet
• Grid factories enable parallel execution of multiple simulation instances
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Genetic Algorithms
• Genetic Algorithms (GAs) are global search algorithms based on the mechanics of natural selection
• GAs are robust search methods:– Can escape local optima– Can deal with ‘noisy’ or ill-defined evaluation
functions
• Some features of GAs are:– GAs search a population of points– GAs use objective function pay-off information– GAs are stochastic
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
A Simple Genetic Algorithm
Generate Initial Population
Fitness Evaluation
Finished?
Selection
Recombination
Mutation
Yes
No
Genetic Algorithm :
generate next generation of solutions for evaluation
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Multi-Objective Optimisation
• Many real-world engineering design problems often involve solving multiple (often conflicting) objectives
• An ideal multi-objective optimisation procedure is:1) Find multiple Pareto
optimal solutions for the objectives
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Multi-Objective Optimisation
• Many real-world engineering design problems often involve solving multiple (often conflicting) objectives
• An ideal multi-objective optimisation procedure is:1) Find multiple Pareto
optimal solutions for the objectives
2) Choose one of the trade-off solutions using higher level information
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Integrated Logistic Support Strategy Optimisation
• MEAROS Optimisation:– Removal of aircraft engines is expensive– By using GAs to optimise soft lives of engine
components in the MEAROS simulation we can develop ‘optimal’ preventative maintenance strategies
• Issues:– MEAROS is a complex stochastic simulation, therefore it
has to be run multiple times for each candidate solution to reduce the effect of random variations
– This requires a lot of computing power
THE GRID !
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
MOGA-G Architecture
Evaluation
Facto
ry S
ervice
Evaluation Instance 1
Evaluation Instance 2
Evaluation Instance n
GA Client
Individual 1
Individual 2
Individual n
Eval Result 1
Eval Result 2
Eval Result n
MO
GA
S
ervi
ce
Generation to be evaluated
Results of evaluation
Create Service Instance
Evaluation
Facto
ry S
ervice
Evaluation
Facto
ry S
ervice
Evaluation Instance 1
Evaluation Instance 2
Evaluation Instance n
GA Client
Individual 1
Individual 2
Individual n
Eval Result 1
Eval Result 2
Eval Result n
MO
GA
S
ervi
ceM
OG
A
Ser
vice
Generation to be evaluated
Results of evaluation
Create Service Instance
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
DAME Use Case
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
DAME Use Case
• In the future:
MEAROS MODEL
Failure Rate Data learnt from DAME E
valuation F
actory
Service
Evaluation Instance 1
Evaluation Instance 2
Evaluation Instance n
GA Client
Individual 1
Individual 2
Individual n
Eval Result 1
Eval Result 2
Eval Result n
MO
GA
S
ervi
ce
Generation to be evaluated
Results of evaluation
Create Service Instance
Evaluation
Facto
ry S
ervice
Evaluation
Facto
ry S
ervice
Evaluation Instance 1
Evaluation Instance 2
Evaluation Instance n
GA Client
Individual 1
Individual 2
Individual n
Eval Result 1
Eval Result 2
Eval Result n
MO
GA
S
ervi
ceM
OG
A
Ser
vice
Generation to be evaluated
Results of evaluation
Create Service Instance
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Security
• The Decision Support System will contain sensitive data, therefore access must be restricted– i.e. Knowledge Base and Engine Model contain
information on the design characteristics and operating parameters of the engine
• Security implemented using Globus Toolkit to provide:– Public Key Encryption– X509 certificates– SSL communications
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Conclusions
• Move from local diagnostic support to centralised, distributed diagnostic support
• Integration of Model-Based FDI, CBR and Optimisation
• Business Benefits :– Reduction in unscheduled maintenance– Reduction in aircraft downtime
Rolls-Royce supported
University Technology Centre inControl and Systems Engineering
UK e-Science DAME Project
Thanks!
The authors gratefully acknowledge thefinancial support of the EPSRC and thevaluable input from engineers at Rolls-Royce and Data Systems & Solutions