Predictive Maintenance of Mobile Mining Machinery: A Case Study for Dumpers

The health of mobile mining machinery is critical to the achieve effectiveness and efficiency in mining production. However, the performance of mobile mining machinery, such as dumpers, is influenced by factors such as the operational environment, machine reliability, maintenance regime, human factor, etc., that lead to the downtime of dumpers. These downtimes have significant consequences on the overall equipment effectiveness (OEE) and lead to decreased capacity, increased maintenance costs and reduces availability. The enablement of prognostics and health management (PHM) can contribute to improve the OEE in mining production.

Conventionally, the existing solutions focus mostly on the reliability and maintainability analysis of dumpers using failure data, maintenance data, operation data etc. Though several existing methods utilize condition monitoring techniques, there is less focus on monitoring the engine vibration and impact the health of the driver. In addition, the existing solutions are not real-time, scalable, or offline-based. Hence, the objective of this paper is to develop a concept for the enablement of PHM for the engine and driver comfort of dumpers. Furthermore, a cloud-based solution for condition monitoring of dumpers has been designed and developed. The solution can be used to assess the engine vibrations and seat vibrations and to estimate the remaining useful life (RUL) of the selected features using standards. The cloud-based architecture is implemented on the AI Factory platform that enable PHM for the improvement of OEE. This platform also facilitates the enablement of a digital twin for components and systems within dumpers or other mobile mining machinery.

Keywords: Predictive maintenance, Digital twin, Dumpers, Condition assessment, Remaining useful life estimation.

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