Methods of Optimization of Mining Operations in a Deep Mine—Tracking the Dynamic Overloads Using IoT Sensor

Abstract

Self-propelled machines are the main resources used by the Polish copper ore mining industry to transport ore from the mining area to reloading points for conveyor transport. Due to the difficult mining conditions, they must meet high requirements in terms of operational efficiency, safety, and reliability. One of the most significant challenges is high robustness to dynamic overloads. In practice, they have a strong dependency on pavement influence during machine movement, the type of operation, and the driving style of the operator. In this research, we focus on the multivariate analysis of dynamic overloads observed on a large population of haul trucks operating in different mining areas. The main aim of this study was the identification of major factors of excessive dynamic overload that result in damage to structural nodes of machines. In the case of haul track, the joint is such a critical component, that in extreme situations, it breaks and splits the machine in two. There are proposed methods for assessing the occurrence of dynamic overload based on recognized mining conditions and operator behavior. In addition, we propose a method to specify which factors are more meaningful for dynamic overloads. A measurement campaign has been conducted using a mobile inertial sensor interconnected with a developing IoT platform for predictive maintenance of mining infrastructure.

Keywords

• Dynamic overloads
• self-propelled machine
• haul truck
• IoT
• inertial sensor
• underground mining