Management a new paradigm for managing your. Joe Mathew

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1 Integrated Engineering Asset Management a new paradigm for managing your assets Joe Mathew

2 CIEAM

3 CIEAM Participants

4 Third Party Participants

5 CIEAM Regional Strategy Research Providers QUT UWA CURTIN Uni UniSA UniNewcastle ANSTO DSTO Monash Uni

6 CIEAM Vision Leading Australian based international research centre Innovative industry directed R&D World-class researchers and practitioners Education & commercialisation Integrated life-cycle asset management Sustainability of Australian industry

7 Asset Management Asset Management (AM) Asset Management is the process of organising, i planning and controlling the acquisition, use, care, refurbishment, and/or disposal of physical assets 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 knowledge-based decision-making software. Assets: Physical infrastructure Industry (Process -refineries i and smelters ) Public Infrastructure (Roads, bridges railways, buildings) Transport facilities (Harbours, airports, strategic facilities) Water and Sewage Facilities Power & Communication Utilities Defence owned assets

8 Concept of Integration R&D Integration Technology Business Systems People Industry Portability

9 Engineering Assets Multidisciplinary Collaboration Life cycle Conceptualisation, design, procurement, manufacture, installation, operation, maintenance, decommissioning/disposal Engineered assets Private/Public Infrastructure/Industry

10 Industry Verticals DEFENCE CIEAM (Asset)

11 CIEAM Research Programs Management Systems and Business Processes OUTCOMES Decision Systems and Models HUMAN FACTORS Systems Integration and IT Diagnostics and Life Prediction Lower cost Longer life Procurement Risk Safety Environment New Sensors Existing Sensors ASSETS

12 CIEAM Programs Models & Decision Systems Advanced Sensors Intelligent Diagnostics & Prognostics Systems Integration Human Dimensions PhD & Masters graduates Professional development Professional accreditation (ISO) Web site & web links Conferences Industry on-site courses Commercialisation Technology Transfer Management of IP Industry participants Industries at large Other CRC s International linkages International Standards SME s Links

13 CIEAM Concept Map of Engineering Asset Management Literature Review on Asset Management Asset Management Principles Asset Management Themes Asset Management Frameworks Asset Management Theory

14 CIEAM Concept Map of Engineering Asset Management Concept of Frameworks A general framework include: strategic planning of assets; asset management decisions; asset ownership/stewardship; p; asset service delivery and risk analysis; asset life cycle costing and budgeting; asset data management; asset condition monitoring; asset engineering and economic analysis; operating and maintaining assets; asset usage life-cycle information; performance measure of assets; assets financiali management.

15 CIEAM IAM Framework

16 Capabilities CIEAM has developed expertise in: 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 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 Comprehensive professional E&T

17

18 EAM 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 remote and distributed systems. There is an ageing infrastructure challenge in many sectors of private and public organisations. Technologies are changing fast and leaving organisations behind in some cases.

19 Key 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 which can bring competitive advantage, improved governance and enhance legislative compliance 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 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 system enhances efficiencies and addresses the skills shortages predicted

20 Methodology: An Integrated Asset Management Decision i Framework Business requirements Asset degradation Degradation alerts Decision Horizon Asset health and cost predictions Decision options Expert knowledg e 20

21 Asset Health-Based Decision Support Diagnosis Condition indicators Environment indicators Reliability profile Integrated reliability prediction Health based maintenance decision + 21

22 Condition Based Prediction (CBP) Definition 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 levels Features Utilises multiple data sources, e.g., condition monitoring, process and performance control, operation and maintenance event records as well as engineering knowledge Uses U an approach that combines both diagnosis/prognosis and reliability models and techniques Updates estimation and prediction of asset health condition continuously Treating asset health prediction as a process!

23 Comparison of Approaches Diagnosis i and Reliability prognosis Theory CM measurements from instruments At fault (event) level - short term and specific Mainly use signal processing algorithms and AI techniques Failure event data Need complete failure history long term and overall Mainly use probability theory and stochastic process models Condition Based Prediction CIEAM approach CM, process/control, event data as well as knowledge, e.g. criticality ranking Prediction as a process calibrated as more observations and event data become available Use combination of diagnostic/prognostic and reliability models and techniques

24 Challenges Complexity in failure mechanism, failure interaction, and failure behaviours (symptoms) Is the phenomenon because of this, or that fault? Observations Copyright 2004 MIMOSA Faults (failures)

25 Challenges (cont d) Complexity in failure propagation and prediction Functional capability Loading change Potential failure Maintenance Abrupt failure Functional failure Current time PF interval Operation Age RUL

26 CBP: Elements Asset audit Data identification and collection Data alignment and analysis Health modelling and profiling Continuous calibration and prediction update Review all elements Based on decision requirement Design intentions Asset hierarchy h and criticality i analysis Component interaction analysis Health indicators Influential/responsive data Static/dynamic CM data Process/performance/event/knowl edge Data pooling from different sources Data alignment with time/event Indicator extraction and analysis Short term diagnosis/prognosis Long term prediction Health modelling considering influential actions Modelling by integrating knowledge Refinement and calibration of Health prediction models when new data/knowledge becomes available

27 CBP: The Models Diagnosis models (SVM, SOM, ES ) Regression models Dependent failure models Stochastic models (BBN, Markov ) More to be researched Condition Based Prediction SVM: support vector machine SOM: self organisation mapping ES: expert system BBN: Bayesian belief network PHM: proportional popoto hazard aadmodel PCM: proportional covariate model Predict both Time to Failure and failure probability in future Make use of CM, process control, reliability and maintenance, engineering knowledge

28 Integration Project Investigation of a standards-based approach for the integration of asset management systems Collaborators University i of South Australia Prof Andy Koronios (PL), Prof Markus Stumptner, Georg Grossmann Queensland University of Technology Prof Lin Ma, Michael Purser, Dr Avin Mathew Assetricity Alan Johnston, Dr Ken Bever ALCIM Jacob George, Toralf Mueller

29 Asset Management Interoperability Standards Industrial automation systems and integration Integration of life-cycle data for process plants including oil and gas production facilities Open Systems Architecture for Enterprise Application Integration

30 Integration Strategy Unstructured Data Analysis Asset Information Management and Data Data Conversion & Information Quality Management Sources Transformation Enterprise Content Condition Management Monitoring i Source Data Hardcopy Native Files 2D & 3D Diagrams Data Preparation Data Analysis Unstructured to Structured Data Transformation XML Asset Information Repository (AIR) XML Enterprise Asset Management MIMOSA Adapters ISO Data Mapping ISO XML Schema MIMOSA & ISO Data Model EAM Adapters (MIMOSA) Data Historians

31 Industry Case Study Australian Nuclear Science and Technology Organisation Objective: Synchronisation of SCADA and condition monitoring data for SAP reporting and asset health prediction Using MIMOSA OSA-EAI for exchange of operation and condition data through Service Oriented Architecture (SOA) Design completed; ready for implementation

32 ANSTO Case Study Architecture Publish Sensor Measurements Sequence Diagram

33 Industry Case Study Queensland Rail Objective: Mapping electrical assets to ISO 15926/MIMOSA OSA-EAI formats to develop a standardised asset register Making asset register available to the organisation via standard web services Possible submissions to ISO RDS/WIP

34 CIEAM Global Initiatives International Society of Engineering Asset Management (ISEAM) World Congress on Engineering Asset Management (WCEAM) International Journal of Engineering Asset Management (IJEAM)

35

36 WCEAM Meetings 1 st WCEAM July 2006 Gold Coast, Australia 2 nd WCEAM June 2007 Harrogate, UK 3 rd WCEAM Oct 2008 Beijing, PR China 4 th WCEAM Sept 2009 Greece 5 th WCEAM Sept/Oct 2010 Australia 6 th WCEAM Portugal 7 th WCEAM Korea

37 Q&A CIEAM PA: Kirsty Hull Tel: Fax:

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