CBR for Fault Analysis in DAME Max Ong University of Sheffield

CBR for Fault AnalysisCBR for Fault Analysisin DAMEin DAME

Max OngMax Ong

University of SheffieldUniversity of Sheffield

http://www.escience-grid.org.uk/index.htm

http://www.epsrc.ac.uk/website/Index.aspx

Distributed Aircraft Maintenance Environment - DAME

OverviewOverview

Overview of presentation• Introduction to Case-Based Reasoning (CBR) technology• CBR for Fault Analysis

– Maintenance Advisor

– Grid Service Implementation

– User interface to the Grid Service

• Related Work– MEAROS

– CBR Workflow Advisor


• CBR is a mature, low-risk subfield of A.I.

• 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

Case-Based ReasoningCase-Based Reasoning

“Reasoning by remembering, reasoning is remembered.”


What is a case?

“A case is a contextualised piece of knowledge representing an experience that teaches a lesson fundamental to achieving the goals of the reasoner.” (Kolodner, 1993)

• Cases link together knowledge that belongs together:

– Information concerning the fault

– Response to the fault

– Effects of those responses

• 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”

Case-Based ReasoningCase-Based Reasoning


Casebase• Repository of fault cases• Cases represent individual engine fault events and maintenance actions

Knowledge Model• Contains general knowledge about a specific field of application• Comprises the data structure (data model), the relationship between values

within the data (valuation models) and information for defining application-specific vocabulary in the data

Valuation Model• Defines the similarities and evaluations of individual pieces of data in relation to

each other• Facilitates knowledge-based, intelligent searches (directed search)

CBR Knowledge CBR Knowledge


CBR Tool Implements• Nearest Neighbour (k-NN) algorithm

EPR[= Index 1]

N1

[= In

dex

2]

Good Condition Engine

BadCondition

EngineNew Case

Close - Up

X

a

X

b

Y

a

Y

b

Case 'a'

Case 'b'

New Case

)(

1

DistanceWeighted'',

2

''

1

''

IndexesAttributesOfNumbern

ntoi

XXWor

XXW

YYWXXWfaCaseNewCaseSimilarityOfDegree

aCasei

NewCaseii

aCasei

NewCaseii

aYaX

CBR SearchCBR Search


Inductive Retrieval

– Induction - ID3 Algorithm

– Knowledge-Guided Induction

Example:

Case 1

Case 2

Case 3

Case 4

Very Good 1.6 100% 500 oK

550 oK

600 oK

500 oK

Good

Very Bad

Bad

1.0

1.1

1.5

106%

100%

105%

EPR > 1.1

Case 1

Case 2 Case 3

Case 4

Yes

Yes No

No

Good ConditionEngines

Bad ConditionEngines

1. Discriminate by EPR

CBR InductionCBR Induction


CBR Maintenance Advisor: Emulates the diagnostic skill of an experienced maintenance

engineer Provides the user with ‘best practice’ advice when confronted

with a set of fault symptoms Provides a confidence measure for each suggested solution Facilitates the processing of logistic data, current and

historical fault data to intelligently isolate an engine problem

Some common misconceptions:x CBR is a relational database of engine faultsx CBR is only a search engine

CBR Maintenance AdvisorCBR Maintenance Advisor


• Why the Grid?

Collaborative working environment (virtual organisation)

Distributed data

Interaction with other services

High performance computing (large casebase computation)

Scalability

Delivery via Grid Services with security




ImplementationImplementation

Import fault and maintenance data from SQL database

Creation of datatypes within CBR

Map fault information to Analysis and Valuation Model

Pre-indexed casebase, knowledge and valuation models are stored in XML




CBR Maintenance Advisor - Grid ServiceCBR Maintenance Advisor - Grid Service

• Deployed as a service on the Grid with Globus 3 Toolkit

• Accessible by user via a web browser across the Internet

• Secure access with user and host authentication, SSL encryption.

• Access to fault information and knowledge gained from fault diagnosis processes

• Obtain maintenance advice in form of appropriate maintenance action


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Quick summary• Case-Based Reasoning (CBR) technology• CBR for Fault Analysis

– “Maintenance Advisor” Service in DAME

– Globus 3 Grid Service Implementation

• Related work– MEAROS

– CBR Workflow Advisor


MEAROSMEAROSModular Engine Arisings & Overhaul Simulation

MOGA MEAROSClient MEAROS

Module Failure Rate Data

Distribute Solutions

Collect Results

Send Population of Solutions

Receive Evaluation Results

Multi-objective Optimisation with Genetic Algorithms :• selection• crossover• mutation• re-insertion

• Interprets the results from MEAROS using a costing model

• Provides a way of distributing solutions to MEAROS and collecting the results

Stochastic model for the simulation of operation, maintenance and supply of gas turbine engines to fleets of aircraft

The module failure rate data is used to improve the failure distributions for the components inside the model.

• Removal of aircraft engines is expensive, significant fraction of operating costs

• MEAROS enables ‘optimal’ preventative maintenance strategies to be determined

• MEAROS within virtual maintenance environment can enhance fleet management of aircraft and engines

• MEAROS simulation is a very compute-intensive process

• The Grid offers high-performance computing resources, enabling faster results and quicker decisions to

facilitate maintenance planning

Documents

CBR for Fault Analysis in DAME Max Ong University of Sheffield