Integrated Engineering AssetIntegrated Engineering Asset Management –

a new paradigm for managing your assetsassets

Joe Mathew

© CIEAM 2008

CIEAMCIEAM

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CIEAM ParticipantsCIEAM Participants

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Third Party ParticipantsThird Party Participants

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CIEAM Regional StrategyCIEAM Regional Strategy Research Providers

QUT

UniSA

UniNewcastleUWA

CURTIN UniANSTO

DSTO Monash Uni

UniSA

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CIEAM VisionCIEAM Vision

• Leading Australian based international research centre

• Innovative industry directed R&D

• World-class researchers and practitioners

• Education & commercialisation

• Integrated life-cycle asset management• Integrated life cycle asset management

• Sustainability of Australian industry

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Asset ManagementAsset Management• Asset Management (AM)

A t M t i th f i i • Asset Management is the process of organising, planning and controlling the acquisition, use, care, refurbishment, and/or disposal of physical assets to optimise their service delivery potential and to optimise their service delivery potential and to minimise the related risks and costs over their entire life through the development and application of intangible assets such as business processes and g pknowledge-based decision-making software.

• Assets: Physical infrastructure

I d t (P fi i d lt )• Industry (Process - refineries and smelters )

• Public Infrastructure (Roads, bridges railways, buildings)

• Transport facilities (Harbours, airports, strategic facilities)

• Water and Sewage Facilities

• Power & Communication Utilities

• D f d t

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• Defence owned assets

Concept of IntegrationConcept of IntegrationR&D Integration

Technology

B i Business Systems

People

Industry Portability

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Engineering AssetsEngineering Assets

• Multidisciplinary

• CollaborationCollaboration

• Life cycle

•Conceptualisation design procurement •Conceptualisation, design, procurement, manufacture, installation, operation, maintenance, decommissioning/disposal

• Engineered assets

•Private/Public

•Infrastructure/Industry

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Industry VerticalsIndustry VerticalsDEFENCE

CIEAMCIEAM(Asset)

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CIEAM Research ProgramsCIEAM Research ProgramsManagement Systems and Business Processes

Systems Decision

OUTCOMES

Systems Integration and

IT

HUMAN FACTORS

Decision Systems

and ModelsLower costLonger life

ProcurementHUMAN FACTORS

Diagnostics and Life Prediction

RiskSafety

Environment

Existing Sensors

Prediction

New Sensors Existing Sensors

ASSETS

New Sensors

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ASSETS

CIEAM ProgramsCIEAM Programs

• Models & Decision Systems

• Advanced Sensors• Commercialisation• Technology Transfer

f• Intelligent Diagnostics & Prognostics

• Systems Integration• Human Dimensions

• Management of IP

• Industry participants• Industries at large• Other CRC’s

• PhD & Masters graduates• Professional development

Professional accreditation • International linkages• International Standards• SME’s Links

• Professional accreditation (ISO)

• Web site & web links• Conferences• Industry on-site courses• Industry on-site courses

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CIEAM Concept Map of Engineering Asset Management

Asset Management Principles

Literature Review on Asset Management

Asset Management Principles

Asset Management Themes

Asset Management Frameworks

A t M t Th

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Asset Management Theory

CIEAM Concept Map of p pEngineering Asset Management

Concept of Frameworks

A general framework include:• strategic planning of assets;• asset management decisions;• asset ownership/stewardship;p/ p;• asset service delivery and risk analysis;• asset life cycle costing and budgeting;• asset data management;• asset data management;• asset condition monitoring;• asset engineering and economic analysis;• operating and maintaining assets;• operating and maintaining assets;• asset usage life-cycle information;• performance measure of assets;

t fi i l t

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• assets financial management.

CIEAM IAM FrameworkCIEAM IAM Framework

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CapabilitiesCIEAM has developed expertise in:

• Strategic Engineering Asset Management (EAM)

Capabilities• Strategic Engineering Asset Management (EAM)

• Policy, Governance and Frameworks

• Integrated asset decision systems

• Business Process Modelling for EAM

• Data Management and Quality

• Interoperability of EAM Systems• Interoperability of EAM Systems

• Degradation sensor technology

• Corrosion, crack and delamination detection

• Condition Monitoring, Diagnostics, Prognostics

• Power Transformer Condition Monitoring

• Infrastructure health monitoring

• Pipeline wear and assessment

• Impact of human factors on IEAM

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Impact of human factors on IEAM

• Comprehensive professional E&T

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EAM 2020 Common DriversEAM 2020 – Common Drivers

• Global markets impacting on Australia in particular via global supply chains.

• Climate change is driving changes in energy sources and the need for energy efficiency.

• Demographics and skills shortages are impinging on how assets will be managed in future and opening up opportunities for more future and opening up opportunities for more remote and distributed systems.

• There is an ageing infrastructure challenge in many sectors of private and public organisations.

T h l i h i f t d l i

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• Technologies are changing fast and leaving organisations behind in some cases.

Key ResponseKey Response• Integrated decision support systems along with the

requisite standards which need to be both national and international

• These systems will support evidence based decision making These systems will support evidence based decision making which can bring competitive advantage, improved governance and enhance legislative compliance

• The systems will require a level of interconnectivity of data • The systems will require a level of interconnectivity of data and decision making that will improve efficiency through a reduction in duplicated effort

• Prognostics systems with predictive models and self • Prognostics systems with predictive models and self diagnostics are required which all require quality data systems for storage retrieval and integrated use

• Focusing these models on more remote and automated management of assets through use of technologies like advanced sensors, wireless, material science based physics of failure models through a web based communication

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of failure models through a web based communication system enhances efficiencies and addresses the skills shortages predicted

Methodology: An Integrated AssetM t D i i F kManagement Decision Framework

Business

Asset

Business requirements

Degradation alerts

Asset health and

Asset degradation Decision

Horizon

Asset health and cost predictions

Expert

20

Decision options

Expert knowledg

e

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Asset Health-Based Decision SupportAsset Health Based Decision Support

Condition Health basedindicators Reliabilityprofile

Integrated reliability prediction

Health based maintenance

decision Environment

indicators

Diagnosis

indicators

21

+

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Condition Based Prediction (CBP)Condition Based Prediction (CBP)Definition

•CBP i f i d di ti •CBP is a process of assessing and predicting the health of engineering assets in the short and long terms in order to effectively support asset management decisions at all support asset management decisions at all levels

Features Treating asset

health prediction as a p ocess!

•Utilises multiple data sources, e.g., condition monitoring, process and performance control operation and

as a process!

performance control, operation and maintenance event records as well as engineering knowledgeU h h bi b h •Uses an approach that combines both diagnosis/prognosis and reliability models and techniques

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•Updates estimation and prediction of asset health condition continuously

Comparison of ApproachesComparison of Approaches

Di i d R li bilit Condition Based Diagnosis and prognosis

Reliability Theory

Condition Based Prediction CIEAM approachCM / l CM

measurements from instruments

Failure event data

CM, process/control, event data as well as knowledge, e.g. from instruments criticality ranking

At fault (event) Need complete failure history

Prediction as a process calibrated as more ( )

level - short term and specific

failure history –long term and overall

calibrated as more observations and event data become available

Mainly use signal processing algorithms and

Mainly use probability theory and stochastic

Use combination of diagnostic/prognostic and reliability models and

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algorithms and AI techniques

and stochastic process models

reliability models and techniques

Challenges• Complexity in failure • Complexity in failure

mechanism, failure interaction, and failure behaviours (symptoms)behaviours (symptoms)

Is the phenomenon

because of this, or that fault?

Copyright 2004 MIMOSA *&^%$#@!Observations

Faults (failures)

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Challenges (cont’d)Challenges (cont d)

• C l it i

Functional capability Loading change

Maintenance• Complexity in failure propagation and Abrupt failure

Maintenance

prediction Potential failure

p

Functional failure

Operation AgeCurrent time PF interval

RUL

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CBP: ElementsCBP: ElementsAsset audit

•Based on decision requirement•Design intentions

hi h d i i li

Data identification and collection

•Asset hierarchy and criticality analysis•Component interaction analysis

•Health indicators

Data alignment and analysis

•Influential/responsive data•Static/dynamic CM data•Process/performance/event/knowledge•Data pooling from different sources

Health modelling and profiling

•Data alignment with time/event•Indicator extraction and analysis

•Short term diagnosis/prognosis•Long term prediction g g

Continuous calibration and prediction update

Long term prediction •Health modelling considering influential actions •Modelling by integrating knowledge

prediction update

Review all elements

•Refinement and calibration of Health prediction models when new data/knowledge becomes available

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CBP: The ModelsCBP: The Models

Diagnosis models (SVM, SOM, ES…)Diagnosis models (SVM, SOM, ES…)

Regression models Regression models

Dependent failure modelsDependent failure models

Stochastic models (BBN, Markov…)Stochastic models (BBN, Markov…)

More to be researchedMore to be researched

Condition Based PredictionCondition Based Prediction

SVM: support vector machineSOM: self organisation mappingES: expert systemBBN: Bayesian belief networkPHM: proportional hazard model

•Predict both Time to Failureand failure probability in future•Make use of CM, process control, reliability and maintenance, engineering knowledge

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p opo t o a a a d odePCM: proportional covariate model

Integration ProjectIntegration Project

• Investigation of a standards-based approach for the integration of asset management

tsystems

• Collaboratorsi i f h li f d i ( )• University of South Australia – Prof Andy Koronios (PL),

Prof Markus Stumptner, Georg Grossmann

• Queensland University of Technology – Prof Lin Ma, i h l i hMichael Purser, Dr Avin Mathew

• Assetricity – Alan Johnston, Dr Ken Bever

• ALCIM – Jacob George, Toralf Mueller• ALCIM Jacob George, Toralf Mueller

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Asset ManagementInteroperability StandardsInteroperability Standards

Industrial automation systems and

15926Industrial automation systems and integration — Integration of life-cycle data for process plants including oil and gas

d ti f ilitiproduction facilities

Open Systems Architecture for Enterprise Application IntegrationApplication Integration

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Integration StrategyUnstructured DataSources

Unstructured DataSources

Data Analysis Data Conversion &Transformation

Data Analysis Data Conversion &Transformation

Asset Information Management and Information Quality ManagementAsset Information Management and Information Quality Management

Integration StrategySourcesSources TransformationTransformation

Condition M it i

Enterprise Content 

M t MonitoringManagement

Enterprise Asset Management

Asset Information R it

Source Data

HardcopyNative Files

2D & 3D

Source Data

HardcopyNative Files

2D & 3D

Data Preparation

Data Analysis

Data Preparation

Data Analysis

Unstructured to Structured Data Transformation

Unstructured to Structured Data Transformation

XML XML

MIMOSAAdaptersg

Repository(AIR) 

2D & 3D Diagrams2D & 3D Diagrams

MIMOSA EAM

Data Historians

ISO 15926Data Mapping

& ISO 15926Data Model

ISO 15926XML Schema

EAM Adapters(MIMOSA)

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Industry Case StudyIndustry Case Study

A t li N l S i dAustralian Nuclear Science and Technology Organisation

• Objective: Synchronisation of SCADA and condition monitoring data for SAP reporting and condition monitoring data for SAP reporting and asset health prediction

• Using MIMOSA OSA-EAI for exchange of Using MIMOSA OSA EAI for exchange of operation and condition data through Service Oriented Architecture (SOA)

• Design completed; ready for implementation

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ANSTO Case Study ArchitectureANSTO Case Study Architecture

Publish Sensor MeasurementsPublish Sensor MeasurementsSequence Diagram

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Industry Case StudyIndustry Case Study

Queensland Rail

• Objective: Mapping electrical assets to ISO 15926/MIMOSA OSA-EAI formats to develop a 15926/MIMOSA OSA-EAI formats to develop a standardised asset register

• Making asset register available to the Making asset register available to the organisation via standard web services

• Possible submissions to ISO 15926 RDS/WIP

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CIEAM Global InitiativesCIEAM Global InitiativesInternational International

Society of Engineering

Asset Management

(ISEAM)

International Journal of

World Congress on

Engineering Asset

Management

gEngineering

Asset Management g

(IJEAM)(WCEAM)

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www.wceam-ims2008.org

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WCEAM MeetingsWCEAM Meetings

1st WCEAM 11-13 July 2006 Gold Coast, Australia

2nd WCEAM 11-13 June 2007 Harrogate, UK

3rd WCEAM 28-30 Oct 2008 Beijing, PR China3 WCEAM 28 30 Oct 2008 Beijing, PR China

4th WCEAM 16-18 Sept 2009 Greece

5th WCEAM Sept/Oct 2010 Australia5th WCEAM Sept/Oct 2010 Australia

6th WCEAM Portugal

7th WCEAM Korea

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Q & AQ & A

•www.cieam.com

•CIEAM PA: Kirsty Hull•CIEAM PA: Kirsty Hull

•Tel: +61 7 3138 1471

•Fax: +61 7 3138 4459•Fax: +61 7 3138 4459

•Email: k1.hull@qut.edu.au

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