The effects of maintenance planning on long-term productivity are not well-studied. Finding the right balance between the costs of preventative maintenance and the disruptions caused by on-site failures is difficult and requires an extensive, systematic exploration of all options. The potential benefits are enormous.

Initial project areas for Theme 3 include:

Optimising Maintenance for Duplicate Assets

Process plants in the mining sector often contain large populations of duplicate assets – for example, precipitators in alumina production. Scheduling the periodic maintenance of these assets is a major challenge because the assets are inter-connected and there is uncertainty around their condition – often the precise maintenance requirements only become known once the asset is taken out of service.

Currently, such maintenance is manually scheduled using “rules of thumb” driven by legacy practices rather than real data or rigorous science.

The aim of this project is to develop fast optimisation algorithms for scheduling maintenance in populations of inter-related duplicate assets, taking into account the condition estimates, constraints on resource/labour availability, production needs, safety compliance, and asset inter-connections and redundancy. This will lead to automated tools for ensuring schedule compliance, cost control, and reducing unplanned maintenance work. Key challenges in the project will include developing the correct optimisation models that add value to the industry partners, and overcoming the dimensionality challenges that are common in large-scale industrial optimisation problems.

Maintenance Scheduling under Plant Constraints

Maintenance plans for mining assets must adhere to numerous constraints ensuring plant integrity and safety – for example, a certain pump cannot be switched off at the same time as another pump. Existing software tools can identify constraint violations and clashes in a given maintenance schedule, but updating the schedule when clashes are detected is still a laborious manual process. Humans are unable to efficiently process the vast streams of data now available, nor can we visualise and balance the numerous competing factors necessary to determine an optimal maintenance schedule that minimises cost.

The aim of this project is to develop mathematical optimisation algorithms for automatically updating short- term and long-term maintenance schedules to avoid violations/clashes while optimising a specific performance index – for example, maximising plant throughput or minimising cost. These algorithms will incorporate tacit rules about which equipment can or cannot be repaired at the same time. Mathematical advances in optimisation theory will be required to deal with the extreme dimensions present in these scheduling problems.

Optimising Maintenance Intervals

All assets – from individual mobile assets to the entire fixed plant – consist of multiple inter-related sub- systems with different maintenance cycle times. A key challenge in maintenance planning is to align these cycle times so that maintenance tasks requiring the same resources and isolations are performed at the same time, minimising rework and disruptions to production. For example, if one component has a cycle time of 6 months and another has a cycle time of 5 months, then it may be advantageous to reduce the 6- month cycle time (effectively over-maintaining the component) so that both components are maintained at the same time. Real-life maintenance projects may involve hundreds or thousands of components, well beyond the scale that humans can comprehend and hence necessitating automated approaches.

To this end, this project will involve developing optimisation algorithms for determining maintenance cycle times in an inter-connected system to maximise synergies, minimise downtime, and minimise the probability of failures. There will be various constraints to respect - for example, in the case of mobile assets, there are typically a limited number of maintenance bays for accommodating equipment undergoing maintenance. Other considerations include sub-system redundancy, journey times, OEM recommendations, and production needs.







The Team

Lead

Chief Investigators

Partner Investigators

Research Fellows

PhD Students

Honours Students


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