PREDICTIVE MAINTENANCE USING ARTIFICIAL INTELLIGENCE: A PATH TO ENHANCED OPERATIONAL EFFICIENCY

Abstract

Unplanned equipment failures continue to result in high costs and safety risks across various industries. Earlier maintenance strategies, such as repairing machines only after breakdowns or servicing them at fixed intervals, are proving insufficient for modern complex systems. Predictive maintenance (PdM) has emerged as a more effective solution, combining artificial intelligence, machine learning, and Internet of Things (IoT) technologies to forecast failures and schedule interventions before disruptions occur. This paper reviews current applications of PdM across various sectors, including manufacturing, energy, aerospace, and healthcare, highlighting evidence of reductions in downtime and maintenance costs. A pilot experiment using a recurrent neural network on a benchmark dataset demonstrated high accuracy in predicting remaining useful life and reduced unnecessary servicing. While issues such as data quality, integration with legacy systems, and workforce expertise remain significant challenges, the findings confirm that AI-driven predictive maintenance represents a practical pathway to improved efficiency, reliability, and sustainability in industrial operations.