Abstract
Healthiness of the conveyor belt is largely determined through physical inspections at regular intervals and time-based maintenance. However, in many cases the conveyor belt damage happens between the actions causing huge production and time losses to the plants, generating a significant personal injury exposure risk.
A right knowledge about its main failure modes will help avoid unexpected and catastrophic failures in conveying systems and provide enough time for maintenance and operation to act in order to take control of the equipment wear (e.g. reducing load) and maintenance planning (e.g. having enough time to prepare the materials and resources for the equipment repair).
The application of modern control and logging systems on the mining sites provides great amount of precious data that normally is underused. If treated properly, can provide the maintenance engineering and reliability groups an important information source about the conveyor system health.
The purpose of this presentation is to explain how some traditional maintenance gaps on the conveyor condition-based monitoring can be filled, through the application of systematic failure modes monitoring, delivering valuable information about potential failures on the conveyor systems and reducing the need of human-based inspections on hazardous areas. Autonomous robotic solution for rollers condition monitoring and enhanced sensors applications on belts will be presented as solutions that are coming to the market, focusing in replace human-based inspections on conveyors, prevents unnecessary people exposure to hazardous areas due to precision maintenance approach and as a consequence reducing catastrophic unplanned failures
The autonomous robotic conveyor roller inspection system collects the individual data of each one of the thousands of conveyor rollers using an enhanced targeting and positioning system and, through periodic inspections, performs the acquisition of thermal, acoustic, and visual data samples to allow the early identification of failure trends in the rollers.
The analysis software provides access to raw data, as well as statistical analysis and the creation of a roller heat map, showing the health of the rollers to guide the best decision making by maintenance teams. All this process targets to provide a suitable time-window for planning and supervision in its daily maintenance actions.
Main expected savings of the predictive approach aligns with studies of the US Dept. of Energy achieving up to 40% over reactive and 12% over preventive maintenance, extending the life of the assets and being safer since it avoids unnecessary people exposure to the field, providing a reliable maintenance time-window for a planned action and an enhanced condition-based approach monitoring the conveyor main components.
As conclusion, the use of the mentioned systematic approach is positive and beneficial in the actual technological context of maintenance evolution and needs to reduce people risk exposure on conveyor areas, providing additional environmental benefits reducing energy consumption due to reduction of friction on falling/damaged and misaligned rollers, reducing environmental sound emission due to damaged rollers or misaligned belt and reducing falling material from the conveyor due to earlier identification of misalignment / slipping control of the belt.