STUDY ON PREDICTIVE MAINTENANCE FOR INDUSTRIAL EQUIPMENT

Abstract

Predictive maintenance (PdM) is a crucial approach in modern industrial operations that enhances equipment reliability and reduces unplanned downtimes by leveraging IoT and machine learning algorithms. This paper explores the methodologies employed in predictive maintenance, focusing on the application of data analytics, sensor integration, and AI-driven failure prediction techniques. The implementation of IoT devices enables real-time data collection, while machine learning models analyze historical trends to detect anomalies. The objective is to create a cost-effective and efficient solution that improves productivity and reduces maintenance costs. This study also presents a case study demonstrating the effectiveness of predictive maintenance in an industrial setting.