Predictive Maintenance Using Machine Learning in Industrial Settings

Abstract

Predictive maintenance (PdM) has become a critical strategy in modern industrial settings, leveraging data-driven approaches to optimize machine performance and reduce downtime. By utilizing machine learning (ML) algorithms, industries can predict failures before they occur, enabling timely interventions and minimizing maintenance costs. This paper explores the application of machine learning techniques in predictive maintenance, focusing on fault detection, condition monitoring, and failure prediction in industrial systems. The paper examines various machine learning algorithms, their implementation in real-world industrial environments, and the benefits of integrating Internet of Things (IoT) technologies. Through an analysis of case studies and recent advancements, this paper highlights the potential of predictive maintenance to enhance operational efficiency, reliability, and cost-effectiveness.