| Abstract Scope |
Belt conveyors are critical assets in mining, directly affecting production continuity, safety, energy efficiency and maintenance cost. This project presents the development of an Asset Intelligence approach, named GPA — Asset Performance Management (Portuguese) — applied to belt conveyor systems in a bauxite mining operation. The solution integrates operational, maintenance, inspection, and condition-monitoring data into a digital environment that supports risk-based decisions and accelerates the transition from reactive maintenance to predictive and prescriptive asset management. The platform consolidates conveyor health indicators, belt thickness monitoring, visual inspection findings, idler temperature profiles, severity classification, degradation trends, and replacement forecasts into interactive dashboards and automated reports. By combining reliability engineering, digital production systems, and industrial data integration, the project transforms dispersed technical information into actionable intelligence. The expected outcome is improved failure anticipation, optimized intervention prioritization, increased asset availability, safer operations, and better lifecycle and capital allocation decisions for critical material handling systems. |