In open-pit mining operations, understanding the operational state of haul trucks is crucial for optimizing productivity, ensuring safety, and improving overall efficiency. Haul trucks operate under a variety of conditions, including loading, hauling, dumping, and idle states, each of which can significantly impact the mining process. Accurate real-time classification of these operational states is essential for effective fleet management, minimizing downtime, and maximizing the utilization of equipment.
Currently, the classification of haul truck states is prone to errors and delays. This project aims to overcome these challenges by developing a machine learning model that leverages GPS data to automatically and accurately classify the operational state of haul trucks in real time. By utilizing advanced machine learning techniques, we can provide more reliable and timely insights into truck operations, thereby enhancing decision-making processes and operational control.
F1 score--> 72.678
