Digital Twin-Enabled Monitoring of Mining Haul Trucks with Expert System Integration: A Case Study in an Experimental Open-Pit Mine

This project builds a digital‑twin monitoring system for mining haul trucks, aiming to make open‑pit operations safer, more efficient, and keep equipment running longer. The system follows a five‑layer design: – **Physical layer** – the actual truck and its sensors – **Data‑acquisition layer** – collects raw sensor signals – **Data‑management layer** – stores and organizes the data – **Application layer** – runs the software that processes the data – **Decision‑support layer** – provides alerts and recommendations With this structure, the system can track each truck in real time and estimate its current condition. An expert system works together with statistical process control (SPC) algorithms to spot unusual behavior early and

to suggest diagnostic steps for mechanical or structural problems. Sensor readings from the truck are cleaned, analyzed, and used to create a live virtual model of the vehicle. Mine operators can view performance metrics on a screen and receive automatic alerts when something is off. The approach is easy to implement and gives undergraduate engineering students hands‑on experience with Internet of Things (IoT) architectures, data‑pipeline management, and rule‑based expert systems. It shows how to tackle real‑world maintenance, production, and safety issues in modern surface mining. Overall, the system acts as a practical guide for deploying cyber‑physical solutions in tough industrial environments.

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