## 5 Discussion
Future research on hybrid edge-cloud frameworks, supported by federated learning and efficient data handling protocols, could further optimize this balance, ensuring robust fault detection without overwhelming system resources. Additionally, bridging domain knowledge from multiple industrial sectors (e.g., aerospace, automotive, and power generation) continues to inform robust strategies [42; 43], particularly in harsh or safety-critical mining environments. Future efforts should prioritize the development of standardized data protocols, cross-validation on larger datasets, and digital twin frameworks that seamlessly integrate with emerging technologies such as 6G wireless, quantum computing, and next-generation sensor arrays [36; 37; 44; 64; 80]. These integrated approaches have the potential to accelerate the mining industry's transition toward a predictive, safe, and environmentally conscious operational paradigm. Industry Implementation and Practical Outcomes. Beyond the theoretical advancements and methodological insights, the findings of this review have direct implications for mining operations seeking to improve efficiency, reduce downtime, and optimize maintenance costs. In large-scale open-pit copper mines, for instance, integrating AI-driven vibration analysis with IoT sensor networks can provide near-real-time visibility into conveyor belt health, mill performance, and haul truck components. By harnessing sensor data on temperature, vibrations, and acoustic emissions, operators can deploy deep learning algorithms to detect early signs of mechanical wear in rotating equipment. This streamlined approach has yielded notable benefits in pilot deployments, reducing unplanned downtime by as much as 15% while lowering maintenance budgets. Moreover, real-time fault diagnosis enables mining companies to schedule targeted interventions during off-peak  
Figure 7: Diagram illustrating the four thematic pillars of Predictive Monitoring in Mining: energy efficiency, sectorial applications, digital twins, and advanced technologies. Each pillar connects to specific operational objectives and technologies. production windows, minimizing the risk of major breakdowns that could halt operations for prolonged periods. Likewise, similar strategies have been adopted in underground coal and hard-rock mines, where ventilation systems and drilling rigs function in challenging, fluctuating conditions. By applying advanced predictive maintenance approaches--such as RNN-LSTM networks for sequence modeling combined with digital twins of ventilation circuits--engineers can optimize airflow and temperature control, enhancing both equipment reliability and worker safety. In some Chilean underground operations, a notable decline in overexposure incidents and measurable improvements in energy consumption have been observed following the implementation of AI-assisted sensors for fan performance monitoring. Meanwhile, in Australian gold mines, machine learning-based fault detection for drilling equipment has led to more accurate scheduling of bit replacements, lowering operational expenses and boosting production continuity. These examples illustrate how integrating AI-driven predictive maintenance into existing operational frameworks delivers immediate, measurable advantages in productivity, safety, and resource utilization. To further substantiate these findings, a structured analysis of key dimensions in predictive maintenance for mining is presented in Tables 3 and 4. These tables categorize the principal technological advancements, sector-specific applications, and methodological challenges that have emerged from this review. Table 3 synthesizes the main discussion points, highlighting how AI-driven methodologies enhance energy efficiency, optimize asset management through digital twins, and improve fault detection across critical mining components.