Rolls-Royce supported University Technology Centre in Control and Systems Engineering UK e-Science...

Rolls-Royce supported

University Technology Centre inControl and Systems Engineering

UK e-Science DAME Project

Alex Shenfield

[email protected]

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.




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




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

• 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

• 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




“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




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

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”



in that situation





in that way

“CASE”



in that situation





in that way

“CASE”



in that situation





in that way

“CASE”





SQL Database

Database Manager

CBR Engine(API)

Service Interface


(Web Browser)

• Interface between application and data

• Reconfigurable





SQL Database

Database Manager

CBR Engine(API)

Service Interface


(Web Browser)

• Contains CBR matching and ranking algorithms





SQL Database

Database Manager

CBR Engine(API)

Service Interface


(Web Browser)

• Processes calls to the CBR service

• Returns results from the CBR service





SQL Database

Database Manager

CBR Engine(API)

Service Interface


(Web Browser)




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

• 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




A Simple Genetic Algorithm

Generate Initial Population

Fitness Evaluation

Finished?

Selection

Recombination

Mutation

Yes

No

Genetic Algorithm :

generate next generation of solutions for evaluation




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




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




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 !




MOGA-G Architecture

Evaluation

Facto

ry S

ervice

Evaluation Instance 1


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




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







DAME Use Case




DAME Use Case

• In the future:

MEAROS MODEL

Failure Rate Data learnt from DAME E

valuation F

actory

Service




GA Client

Individual 1

Individual 2

Individual n

Eval Result 1

Eval Result 2

Eval Result n

MO

GA

S

ervi

ce




Evaluation

Facto

ry S

ervice

Evaluation

Facto

ry S

ervice




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







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




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




Thanks!

The authors gratefully acknowledge thefinancial support of the EPSRC and thevaluable input from engineers at Rolls-Royce and Data Systems & Solutions

Date post:	28-Mar-2015
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