REM - REMOTE EQUIPMENT MONITORING, DIGITAL ASSET MANAGEMENT SOLUTIONS FOR INTERCONNECTED MINING F.J. Morales1, J. Caceres2, G. Cubas3, 1 Mechatronics Engineer, Catholic University of Santa Maria (Per), 2Mechanical/Electrical Engineer, University of Lima (Per) 3 M.Eng. Electronic /Mechanical Engineer, Del Norte Uni (Col),UTB (Col), MIT (USA), UCSP (Per) ABSTRACT Mining companies operate in high‑complexity, data‑driven environments that require real‑time monitoring, advanced analytics, and automated systems to manage geotechnical variability and optimize production. To maintain operational integrity, the industry must implement technologies that enhance safety performance, improve process efficiency, and ensure environmental compliance through predictive models, integrated IoT platforms, and digitally controlled workflows. Remote Equipment Monitoring (REM) provides a unified digital ecosystem that integrates equipment health data, performance indicators, and predictive analytics for smarter asset management. Using edge computing, IoT sensors, and cloud analytics, REM delivers near real‑time insights and early warnings based on high‑resolution telemetry. This enables mining operators to detect performance issues and potential failures well in advance, reducing unplanned downtime, lowering maintenance costs, and improving equipment productivity. This modular technology and scalable architecture adapt to different mining contexts, supporting both self‑managed analytics and fully managed services. Its interoperability with fleet management, reliability engineering, and enterprise platforms creates a seamless digital layer across operations. The roadmap emphasizes automation, data governance, and global standards for secure, data‑driven decision‑making, targeting improvements such as a data quality compliance over 95%, and system integration uptime exceeding 99%. Field deployments demonstrate clear operational gains, including maintenance cost reductions of 10–20%, increases in equipment availability above 90%, and downtime reductions of up to 15%, supported by reliability metrics such as higher MTBF, lower MTTR, and strengthened cross‑functional collaboration measured through shorter issue‑resolution cycles and greater digital workflow adoption. With unified dashboards, predictive models, and automated workflows, REM shifts mining operations to proactive, data‑driven control. It reduces decision latency, improves asset reliability, and enables early anomaly detection, ensuring consistent compliance and operational stability. KEYWORDS
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