APPLICATION OF PREDICTIVE MAINTENANCE IN MANUFACTURING WITH THE UTILIZATION OF AI AND IOT TOOLS

Abstract

Predictive maintenance (PdM) represents a paradigm shift in how maintenance activities are performed in the manufacturing sector. Unlike traditional reactive maintenance, which addresses issues after failures occur, or preventive maintenance, which follows a fixed schedule, predictive maintenance leverages advanced technologies like Artificial Intelligence (AI) and the Internet of Things (IoT) to predict when equipment is likely to fail. The key significance is to use IoT sensors, real-time data such as temperature, vibration, pressure, and usage patterns are collected from machinery. This data is then analyzed by AI algorithms to identify anomalies, trends, and failure precursors, enabling organizations to act before breakdowns happen. This approach minimizes unplanned downtime, reduces repair costs, extends the operational life of assets, and improves overall production efficiency. Predictive maintenance has become increasingly vital in modern manufacturing due to the complexity of industrial equipment and the rising demand for efficiency and reliability. Its application has shown significant benefits across various industries, including automotive, aerospace, and heavy machinery. With the rapid evolution of AI and IoT technologies, predictive maintenance is poised to become an indispensable tool in achieving lean, cost-effective, and resilient manufacturing processes. This research explores the methodologies and future potential of predictive maintenance, particularly within electric vehicle (EV) manufacturing, where precision and uptime are critical.