Managing Mine Road Maintenance Interventions Using Mine Truck On-Board Data

The management of unpaved mine road networks on large surface mines rarely results in optimal road maintenance strategies and minimised total road-user costs. This is ascribed mostly to the complex and dynamic combination of variable road networks and loading and discharge points. In a dynamic mining environment - typically those mines in which production is managed by a centralised truck dispatch system ? there is no guarantee that a particular road maintenance intervention will contribute significantly to reducing total road-user costs or increasing productivity. Most large surface mines operating ultra-heavy mine trucks rely on an integrated on-board diagnostic data collation, communication and GPS-asset location system as a real-time fleet management tool. By extending this system to incorporate a multi-sensor analytical procedure in which specific truck vital signs are monitored and filtered, a road defect can be recognised and a trigger level set to relay the location of defects on the mine haul road to the centralised truck data management system. Previous work established the feasibility of the multi-sensor approach to road defect recognition on large mine haul trucks, and outlined the defect recognition, analytical and modelling issues and system limitations. This paper presents the development of the analytical procedure used as a basis for evaluating the truck on-board data to establish maintenance priorities amongst a network of mine roads. Following an introduction to the system architecture, the results of system field trials are analysed and the results discussed in the light of defect density and traffic volume as the primary variables in an approach to prioritising road maintenance. The paper concludes that by supplementing the existing mine communication and asset management systems, road maintenance can be managed on a near real-time basis and maintenance equipment dispatched to where most immediate benefit will realised from a maintenance intervention on the network, thereby generating the maximum improvement in service and reduction in cost per ton hauled.