316 1. INTRODUCTION AND OBJECTIVES The mining industry faces simultaneous pressures stemming from market volatility, ever-evolving environmental regulations, and specialized talent gaps. These conditions increase the need to improve operational reliability, end-to-end visibility, and speed of decision-making. 1.1. Mining Challenge Equipment fleets trucks, shovels, drills, and ancillary units generate large volumes of telemetry data. However, the fragmentation of systems and processes limits advanced analytics and standardization of information. Closing that gap requires a unified digital layer that normalizes data, enables consistent insights, and orchestrates actions between maintenance, operations, and reliability. Compliance with more stringent environmental laws demands greater traceability, energy efficiency and emission control, reinforcing the need for robust digital infrastructure. In turn, social sustainability requires strengthening interaction with communities, guaranteeing transparency and continuous monitoring of impacts.(Banaee et al., 2013) Finally, the transition to more automated and data-driven operations depends on the development of human talent with digital, analytical and management skills in environments of high technological complexity. 1.2. State of the Art: Condition Monitoring, Predictive Analytics and Remote Operations in Mining: Mining digitalization has advanced through the integration of online monitoring systems, industrial IoT, and cloud analytics, allowing real-time operational data to be captured to improve the reliability and performance of critical equipment. These capabilities enable condition-based maintenance (CBM) strategies, supported by techniques such as vibration analysis, thermoacoustic monitoring, and structural evaluation, widely recognized for their effectiveness in the early detection of mechanical failures. In parallel, the consolidation of integrated control centers has transformed operational supervision and coordination, integrating data from SCADA, FMS and geotechnical systems. The adoption of digital twins emerges as a natural extension, allowing asset behaviors to be simulated and maintenance strategies in virtual environments to be evaluated before implementation. (Shimaponda-Nawa & Nwaila, 2024) Figure 5 Integrated Operation Center room The contemporary approach prioritizes collaboration between human experts and analytical systems. While digital platforms automate anomaly detection and alert prioritization, contextual validation still depends on specialized judgment, under reliability, security, and risk management frameworks. 1.3. ¿What is the Problem? Despite these advances, many sites still struggle with data fragmentation (multiple OEM tools, inconsistent tags, limited context), reactive maintenance (late detection of degradation, firefighting work orders, excess downtime), inconsistent decision pathways (varied practices between shifts, assets, and sites), and scalability limits (solutions that work in pilots but fail to scale across fleets or regions). This results in avoidable unplanned events, inflated cost per hour/ton, and underutilized analytics investments.
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