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Modelling, Optimisation and Decision Support Using the Grid. Alex Shenfield a.shenfield@sheffield.ac.uk. Rolls-Royce University Technology Centre in Control & Systems Engineering Department of Automatic Control & Systems Engineering The University of Sheffield, UK. Introduction :

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slide1

Modelling, Optimisation and

Decision Support

Using the Grid

Alex Shenfield

a.shenfield@sheffield.ac.uk

Rolls-Royce University Technology Centre in Control & Systems Engineering

Department of Automatic Control & Systems Engineering

The University of Sheffield, UK.

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

Overview of Presentation
introduction to dame
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
project partners
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.
motivation for dame
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
case based reasoning

“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

ProposeSolution

Adapt

Justify

Criticize

Evaluate

Store

cbr maintenance advisor
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
cbr engine architecture

SQL Database

“CASE”

“CASE”

“CASE”

“CASE”

Database Manager

  • Description of situation
  • Description of situation
  • Description of situation
  • Description of situation

CBR Engine

(API)

  • Description of problem
  • in that situation
  • Description of problem
  • in that situation
  • Description of problem
  • in that situation
  • Description of problem
  • in that situation
  • Description of how
  • problem was addressed
  • Description of how
  • problem was addressed
  • Description of how
  • problem was addressed
  • Description of how
  • problem was addressed

Service Interface

  • Results or outcome of
  • addressing the problem
  • in that way
  • Results or outcome of
  • addressing the problem
  • in that way
  • Results or outcome of
  • addressing the problem
  • in that way
  • Results or outcome of
  • addressing the problem
  • in that way

Grid/Web Service

Client

(Web Browser)

CBR Engine Architecture
cbr engine architecture9

SQL Database

Database Manager

CBR Engine

(API)

Service Interface

Grid/Web Service

Client

(Web Browser)

CBR Engine Architecture
  • Interface between application and data
  • Reconfigurable
cbr engine architecture10

SQL Database

Database Manager

CBR Engine

(API)

Service Interface

Grid/Web Service

Client

(Web Browser)

CBR Engine Architecture
  • Contains CBR matching and ranking algorithms
cbr engine architecture11

SQL Database

Database Manager

CBR Engine

(API)

Service Interface

Grid/Web Service

Client

(Web Browser)

CBR Engine Architecture
  • Processes calls to the CBR service
  • Returns results from the CBR service
cbr engine architecture12

SQL Database

Database Manager

CBR Engine

(API)

Service Interface

Grid/Web Service

Client

(Web Browser)

CBR Engine Architecture
model based fdi
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
engine modelling and simulation service
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
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

Genetic Algorithms
a simple genetic algorithm

Generate Initial Population

Fitness Evaluation

Yes

Finished?

No

Genetic Algorithm :

generate next generation of solutions for evaluation

Selection

Recombination

Mutation

A Simple Genetic Algorithm
multi objective optimisation
Many real-world engineering design problems often involve solving multiple (often conflicting) objectivesMulti-Objective Optimisation
  • An ideal multi-objective optimisation procedure is:
    • Find multiple Pareto optimal solutions for the objectives
multi objective optimisation18
Many real-world engineering design problems often involve solving multiple (often conflicting) objectivesMulti-Objective Optimisation
  • An ideal multi-objective optimisation procedure is:
    • Find multiple Pareto optimal solutions for the objectives
    • Choose one of the trade-off solutions using higher level information
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

Integrated Logistic Support Strategy Optimisation

THE GRID !

dame use case22
DAME Use Case
  • In the future:

Failure Rate Data learnt from DAME

MEAROS MODEL

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

Security
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

Conclusions
thanks
The authors gratefully acknowledge the

financial support of the EPSRC and the

valuable input from engineers at

Rolls-Royce and Data Systems

& Solutions

Thanks!