ARTIFICIAL INTELLIGENCE IN PREDICTIVE MAINTENANCE FOR INDUSTRIAL EQUIPMENT: OPTIMIZING EFFICIENCY AND REDUCING DOWNTIME

Abstract

The application of Artificial Intelligence (AI) in predictive maintenance (PdM) has the potential to revolutionize industrial operations by enhancing equipment efficiency and reducing downtime. Predictive maintenance leverages AI algorithms to predict equipment failures before they occur, enabling timely interventions that prevent costly breakdowns. This paper explores the integration of AI into predictive maintenance systems, its benefits, challenges, and its impact on operational efficiency in various industries. A review of recent studies highlights the improvements in equipment reliability, cost savings, and productivity. Additionally, this paper discusses the AI methodologies used in predictive maintenance, including machine learning, deep learning, and neural networks, as well as the implementation challenges that organizations face. The findings suggest that AI-powered predictive maintenance can significantly enhance industrial productivity, reduce costs, and optimize the lifecycle of critical equipment.