Investigating how to create a FAIR, commercially sensitive data collection.
The Australian Research Council (ARC) Industrial Training Transformation Centre for Transforming Maintenance through Data Science (CTMDS) plans to introduce timely and cost-efficient maintenance scheduling by developing data-intensive mathematical and computational algorithms for improving asset management and a capability for fault prediction.
This project will investigate how to create a mining industry machine and vehicle maintenance data collection that meets the commercial sensitivity needs of industry partners, as well as being Findable, Accessable, Interoperable and Reusable (FAIR). This will enable the development of new methods and tools to improve productivity and asset reliability for the industry.
Who is this project for?
- Peak bodies
- Research organisations
- Government (state and commonwealth)
What does this project enable?
A FAIR machine maintenance data collection will enable researchers to develop new methods and tools to transform the maintenance management process, increasing both productivity and profitability through automation of maintenance stages. By optimising maintenance schedules and improving the ability to predict faults, this research can help to increase longevity of equipment, reduce environmental impacts, and overall provide a safer work environment for mine site maintenance workers.
- FAIR assessment [PDF 191KB]
- Final Report [PDF 93KB]
- Presentation [PDF 155KB]
- Centre for Transforming Maintenance through Data Science