Project

Predictive Maintenance for Building Electrical Assets 


Host university

QUT

INDUSTRY PARTNERS

THEME ALIGNMENT

Data collection, security, and integration

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Modelling, Simulations and Prognostics

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Infrastructure Health Monitoring and Predictive Maintenance

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INDUSTRY PROBLEM

Fredon is a member of the Asset Institute and a leading provider of engineering, installation and maintenance solutions through our Asset Services, Electrical & Communications, HVAC, Technology, Security and Infrastructure divisions. A core part of Fredon’s business is electrical services including the design and installation of lights, batteries, and building systems control. Fredon has contracts to maintain these assets as well. Yet, maintenance plans for these assets are typically based on conservative, inflexible, and not rigorously justified schedules. Moreover, such time-based approaches do not adjust to the in situ condition degradation. This project will develop tools to move Fredon towards a more data-driven predictive approach, including remaining useful life (RUL) estimation of key assets and incorporating this information into optimal maintenance decision models.  

 


CONTACT CONTRIBUTORS

Michael E. Cholette

Chief Investigator
Queensland University of Technology
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Tommy Chan

RIIS Hub Lead Chief Investigator
Queensland University of Technology
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Joe Mathew

Partner Investigator
Asset Institute
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Haohao Shi

HDR Student
Queensland University of Technology
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