Developing Intelligent Systems for Predictive Maintenance in Industrial Environments

Abstract

This study explores the development of intelligent systems aimed at optimizing predictive maintenance in industrial settings. By employing machine learning algorithms and real-time data analysis, these systems can predict equipment failures before they occur, thus reducing downtime and maintenance costs. The paper discusses various models and methodologies, providing insights into implementing these technologies for enhanced operational efficiency.

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