## 5 Discussion
This approach has reduced the time required for troubleshooting and improved decision-making accuracy for frontline engineers [48, 49]. Despite these advancements, the deployment of digital twins remains constrained by the need for consistent, high-quality data and validated physical models [57, 64]. Addressing multi-physics phenomena, such as fluid-structure interactions in hydrotransport pipelines, requires a combination of sensor arrays and robust data assimilation techniques [37, 61, 86], which can be costly and technically complex for smaller mining operators. Fundamental Technologies and Advanced Methodologies: Highlights the role of _Deep Learning_, _Hybrid AI_, and _IoT Sensors_ in scaling predictive maintenance. The integration of large-scale data with robust fault detection algorithms is essential for harsh mining conditions, shaping intelligent maintenance ecosystems across operations [36, 37, 38, 49, 50]. For instance, advanced RNN-CNN hybrids have been effective in extracting subtle temporal-frequency features from unsteady signals [31, 73], while fractional-order PID controllers have demonstrated their ability to manage strongly nonlinear dynamics [86]. The inclusion of distributed ledger technologies (DLT) [40, 41, 42] has also been explored to ensure data integrity and secure multi-party collaboration, underscoring the emerging intersection of industrial IoT with trustless data management [43, 44]. Research into these layered architectures is expected to facilitate more open innovation ecosystems, particularly in scenarios where equipment servicing is contracted to multiple OEMs [38, 89]. Overall, integrating IoT sensors with hybrid AI approaches--combining sensor data with physics-based models--has enhanced fault detection accuracy and system scalability. Digital twin modeling has been employed to forecast asset behavior under diverse conditions, reducing unplanned downtime in complex mining sites. However, the lack of standardized datasets continues to hinder replicability and cross-environment adaptability, while high computational costs and interoperability constraints remain significant challenges [39, 40, 41]. Furthermore, advanced optimization frameworks [43, 44] have been tested in open-pit haulage operations to determine real-time speed settings for trucks, merging predictive maintenance with dynamic routing [46, 47]. This integration reinforces the synergy among key operational pillars, as illustrated in Figure 7, by continuously feeding data-driven insights back into scheduling algorithms and digital twins [8, 9, 64]. Building on prior work, this review underscores the versatility of hybrid approaches in addressing complex mining demands, combining physical insights with deep learning techniques. Key gaps include real-time IoT integration, interpretability of machine learning models, and large-scale validation. Strengthening data standards, scaling field tests, and fostering collaborative frameworks can enhance the development of effective solutions. As Mining 4.0 progresses, the collaboration between domain experts, data scientists, and academia is expected to drive more sustainable and efficient operations [10; 11; 13; 19; 20; 51; 62; 85]. A critical aspect of deploying predictive maintenance systems in mining involves balancing model accuracy with computational feasibility. Although cloud-based architectures can handle large-scale data processing and offer advanced machine learning capabilities, they may face latency, bandwidth, or connectivity limitations in remote mine sites. Conversely, edge-based solutions enable real-time analytics with reduced latency and localized decision-making but can be constrained by hardware capacity or power consumption. Several studies reviewed here leverage lightweight models or online feature extraction to address these constraints, aligning model complexity with available on-site resources [18; 31; 59].