Abstract
Equipment stands as a crucial organizational asset for sustaining mining productivity and efficiency. Effective maintenance programs are essential to ensure equipment health and reduce breakdown risks while maximizing reliability, availability, and maintainability. Traditional mining maintenance strategies, either reactive (responding after breakdowns) or periodic (aiming to prevent faults), often result in costly downtime, expenses, and production losses. Monitoring equipment health is critical to prevent failure, minimize downtime, and inform part replacement. Real-time data gathered through emerging sensor technologies convey significant opportunities. However, the mining industry has not developed the approaches to process and glean information from these data. To address this gap, the present study proposes predictive maintenance techniques for mining equipment based on machine-learning models. Through analysis of real-time sensor data and historical maintenance logs, this approach accurately predicts future failures by investigating root causes and modelling temporal patterns. The implementation of this initiative optimizes maintenance efficiency and resource management. This approach will also enhance worker safety, maximize environmental compliance, and support cost-effective operations, as well as align with the commitment to the mineral industry's sustainable growth.