APPLICATION OF ARTIFICIAL INTELLIGENCE IN RELIABILITY ENGINEERING AND PREDICTIVE MAINTENANCE: A CASE STUDY IN THE MINING INDUSTRY

Abstract

This article explores the application of artificial intelligence (AI) in reliability engineering and predictive maintenance, emphasizing its importance within the context of Industry 4.0. Through a case study conducted in a mining company, wireless triaxial sensors were used to monitor real-time vibration and temperature in critical equipment, such as calamine mill gearboxes. The AI platform analyzed the collected data, identifying patterns and anomalies that indicated imminent failures. Based on the generated alerts, the maintenance team carried out preventive interventions, avoiding catastrophic failures and saving operational costs. The study demonstrates that AI not only optimizes maintenance processes and extends asset lifespan, but also enhances operational efficiency and strategic decision-making. This technological advancement facilitates a shift from reactive approaches to proactive asset management strategies, maximizing the reliability and safety of industrial systems.