Implementing Big Data Analytics and Machine Learning for Predictive Maintenance in Manufacturing Facilities in South Korea

Abstract

Predictive maintenance utilizing big data analytics and machine learning has emerged as a promising approach to optimize maintenance strategies and reduce unplanned downtime in manufacturing facilities. This paper provides a comprehensive overview of implementing predictive maintenance solutions in manufacturing plants in South Korea. It begins by highlighting the challenges of traditional preventive and reactive maintenance approaches. The paper then introduces big data analytics and machine learning as enablers for transitioning to predictive maintenance. Current applications, benefits, and challenges of implementing predictive maintenance in manufacturing are discussed. The core sections provide practical guidelines for collecting and integrating data from industrial assets, applying machine learning algorithms, and deploying predictive maintenance systems. Factors unique to manufacturing facilities in South Korea, such as high automation rates and nationwide 5G coverage, are considered. Detailed examples of using sensor data and machine learning algorithms like classification, regression, and deep learning for equipment maintenance are presented. The paper concludes by proposing a roadmap for manufacturing plants in South Korea to leverage big data and analytics to optimize maintenance strategies, minimize downtime, reduce costs, and improve overall equipment effectiveness.