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. Figure 22. Relationships of digital transformation and problems to be addressed 1.4. Objetives This paper analyzes the design and implementation of a technological solution for remote equipment monitoring (REM) in mining, focused on integrating asset health data, predictive analytics and real-time monitoring. This proposal aims to build a modular and scalable digital architecture that reduces unplanned failures, optimizes maintenance costs and improves productivity through an interoperable and management-oriented system. 1.4.1. General Objective: Design and implement a progressive REM model that improves safety, reliability, and cost control by enabling proactive and governed equipment health management on scale. 1.4.2. Specific Objectives: • Unify data flows from OEM systems, IoT sensors, and enterprise platforms within a governed semantic layer. • Detect early signs of equipment deterioration through multi-signal analytics and predictive modeling with measurable alert quality. • Operationalize insights via automated notifications, risk scoring, and workflow integration to maintenance execution. • Enable progressive service levels (from site-run analytics to fully managed services) to match organizational readiness. • Build workforce capabilities in remote monitoring, reliability analytics, and cross-functional incident response.
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