| Abstract Scope |
This paper explores asset management and reliability engineering's role in optimizing industrial assets at a Brazilian bauxite mining company. It emphasizes improving operational performance by boosting asset availability, cutting capital costs, and enhancing safety. The study integrates event historians, machine learning (ML), and artificial intelligence (AI) to advance asset management, benchmarking against global mining companies' return-on-investment rates. It highlights Industry 4.0 tools tailored for mobile assets like tractors and trucks, combining established solutions with innovative approaches. A key application uses an event historian for time-series data and custom ML/AI algorithms to predict equipment failures, optimize maintenance, and enhance equipment effectiveness, reducing costs. The paper details ML for analyzing datasets and AI for strategic decision-making and automation. Case studies show improved equipment performance and cost efficiency. It also addresses challenges, future standardization, and benefits like enhanced performance, financial gains, better technician training, and improved safety. |