317 Figure 6. Relationships of digital transformation and problems to be addressed 1.4. Objectives 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. 2. METHODOLOGY Ensure that the monitoring systems enable all instrumented equipment to transmit reliable, continuous, and properly conditioned data to the REM ecosystem platforms, supported by an engineered design that guarantees full compliance and seamless integration with the mine’s standardized monitoring and operational methodology.
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