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Engineering Asset Management and Systems Engineering

Overview

The Engineering Asset Management Group at the University of Wollongong (UOW) provides leading research that supports a unique postgraduate education program. Our research outcomes deliver to key Australian industries methods of sustaining their strategically significant infrastructure cost effectively. The group members are actively engaged in a number of fundamental and applied research projects in collaboration across the world with industries in various areas of railways, energy pipeline, steel industry, utilities, wind turbines and SMART Grid. This research covers all aspects of engineering asset management from component life analysis and system reliability engineering and maintenance program development to management system structure and performance measurement and management. Our objective is to drive scientific approaches to engineering asset management by focusing on achieving outcomes for industry.

This group has developed very strong expertise in systems engineering and engineering asset management in terms of modelling, methods and decision making which covers statistical data modelling, reliability engineering, condition prediction, diagnosis and prognostics, process simulation and optimisation. The group has real practices in research for railway, energy pipelines and wind energy industry. These include a range of activities from reviewing current management practices associated with asset management to specific development and application of reliability analysis to asset management decisions including: remaining life determination; maintenance program decision processes and decision support systems; and replacement decision processes and associated decision models. Asset management systems are concerned with the planning and control of all asset-related activities and their relationships to ensure that asset performance meets the intended competitive strategy of the organisation. All aspects related to asset life cycle activities from concept to disposal are crucial to the success of an organisation. These activities are naturally both interdisciplinary and interrelated.

Vision

  • To be a well-known and competitive research group providing the long term education and research needs of industry.

Mission

  • To provide industry and society to sustain the infrastructure assets required to achieve a safe liveable and economically viable environment.
  • To drive a scientific approach to engineering asset management focused on achieving outcomes for industry.

Research Overview

The future research challenges in engineering asset management include life cycle management of assets including incorporating all practically available data into an integrated decision support system, developing asset management support decision models to enhance trade off among the alternative options, establishing a systematic way for maintenance program development, and incorporating life-cycle cost and risk assessment techniques informed by predicted performance into the overall asset management system of organisations; application of systems engineering methods to large and complicated systems; fault diagnostics & prognostics; condition based maintenance and operation with statistical/stochastic models development for condition prediction and management modelling; system dynamics simulation and modelling with application to large and complicated systems including wind farm, Smart Grid and energy storage system.

Research Directions

  • Life Cycle Management of Assets
  • Application of Systems Engineering Methods to Large and Complicated Systems
  • Fault Diagnostics & Prognostics
  • Optimizing Asset Management Practice
  • Statistical and Stochastic Models Development for Condition Prediction
  • Systems Dynamics Simulation Modelling
  • Asset Data Management for Decision Making
  • Asset Management Support Frameworks and Models
  • Life Cycle Cost and Risk Management Models 

Current Research Projects

  • Prediction-Based Decision Support Framework for Energy Pipelines (EPCRC project, 2015 ~ 2018)
  • Pipeline Operational Life Prediction by Neural Networks (EPCRC project, 2013 ~ 2015)
  • Benchmarking Pipeline Corrosion for Life Prediction (EPCRC project, completed)
  • Predicting Service Potential of Railway Bridge (Railway CRC project, completed)
  • Improved Railway Noise Management (Railway CRC project, completed)
  • Railway Noise Reduction (Railway CRC project, completed)
  • Integration of Condition Monitoring Data into Railway Asset Management

Research Project Proposals Developed

  • A System for Condition Based Operation of Wind Turbines (DP160104798, under review)
  • Integration of Condition Monitoring Data into Railway Asset Management (ARC linkage project proposal)
  • Benchmarking Energy Pipeline Risk Focused on Integrity Management (EPCRC project, will be put into next round of panel review in 2015)

Research Direction and Industry Collaboration

  • Life Cycle Management of Assets
  • Application of Systems Engineering Methods to Large and Complicated Systems
  • Fault Diagnostics & Prognostics
  • Optimizing Asset Management Practice
  • Statistical/Stochastic Models Development for Condition Prediction and System Dynamics Simulation Modelling
  • Asset Data Management for Decision Making
  • Asset Management Support Frameworks and Models
  • Life Cycle Cost and Risk Management Models

PhD Research Areas Available

The future PhD research topics may include:

  • Smart Grid operational management
  • Deterioration modelling for asset condition prediction by Markov Chain Monte Carlo simulation
  • Application of systems engineering methods to optimisation in large and complicated systems
  • Fault diagnostics & condition based operation of wind turbines
  • Energy storage system operational management
  • Energy storage study by molecular dynamics simulation
  • Energy pipeline condition prediction associated with external corrosion
  • Railway noise control and management We encourage self-motivated students to join the group to conduct research in these or other related areas.

Contact Richard Dwight or Tieling Zhang.

Master Degree Programs

Courses/Subjects Which we Can Offer

  • Data Analysis and Modeling
  • Reliability Engineering
  • Applied Probability and Statistics
  • Mathematical Modeling & Simulation
  • Design of Experiments
  • Modelling of Engineering Asset Management
  • Asset Lifecycle and Risk Management
  • Logistics and Operations Management
  • Engineering Asset Management
  • Advanced Mechanical Vibration Theory
  • Measurement and Analysis of Mechanical Vibration
  • Rolling Stock Maintenance
  • Systems Engineering

Industry Collaboration

Our current industry collaborators include energy pipeline companies under Energy Pipeline EPCRC programs and railway organisations supporting through CRC projects for Railway Innovation in Australia and Hong Kong MTR.

Research Collaborators

Our research collaborators include

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