Simulation Tools for Transport Monitoring Systems in the Mining Industry

Abu-Abed, Fares · 2021 · Crossref

DOI: 10.1051/e3sconf/202127801017

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Summary

This paper addresses the challenge of monitoring vehicle fleets in the mining industry, a sector characterized by high risks to human health and costly equipment damage. While Internet of Things (IoT) technologies have advanced transport monitoring in logistics, existing solutions are often unsuitable for mining due to specific vehicle requirements, security policies, and telemetry interfaces. The authors propose a computer modeling approach to simulate fleet monitoring systems, aiming to reduce prototyping costs and minimize vehicle downtime associated with testing physical equipment. By validating monitoring metrics in a simulation environment before deployment, the method seeks to lower the risk of initial launch failures and improve system reliability. The study utilizes the open-source traffic simulation software SUMO to model a mining site, specifically using a satellite image of the Bingham Canyon mine in Utah to construct a road network. The simulation is controlled externally via a Python program using the TraCI API, which allows for the creation of vehicles, assignment of routes, and real-time data extraction. Telemetry data, including vehicle acceleration and steering angle, is collected at regular intervals and stored in InfluxDB, a time-series database chosen for its integrated dashboard capabilities and suitability for IoT applications. The workflow involves data collection from the simulation, transfer via the API, and storage in the database, effectively mimicking the behavior of real embedded telemetry devices by aggregating measurements. The results demonstrate the feasibility of this integrated workflow. The authors successfully visualized simulated telemetry data, plotting metrics such as median truck acceleration and the standard deviation of steering angles. These visualizations allow for the analysis of vehicle movement characteristics and driver behavior patterns. The high values observed for steering angle deviations were attributed to the sharp trajectory angles defined in the model. The study confirms that SUMO, combined with TraCI and InfluxDB, can effectively simulate the collection and storage of fleet telemetry data, providing a functional environment for testing monitoring configurations without physical hardware. The significance of this work lies in its potential to streamline the development of mining fleet management systems. By enabling the testing of monitoring metrics and data transmission protocols in a virtual environment, organizations can optimize system configurations and identify relevant performance indicators before incurring the costs and operational disruptions of physical prototyping. The authors conclude that this approach supports efficient data transmission and storage, suggesting future improvements such as incorporating additional metrics like vibration and humidity, implementing GPS coordinate tracking, and developing metric scoring systems to aid decision-making. This methodology offers a scalable and cost-effective pathway for enhancing safety and operational efficiency in mining transport operations.

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• exposure measurement

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• Methodological Resource: tool software