SCADA-Enabled Predictive Maintenance Framework for Cogeneration Systems in American Manufacturing Facilities

Abstract

This paper presents a comprehensive review of a SCADA-enabled predictive maintenance framework for cogeneration systems in American manufacturing facilities. The study explores how Supervisory Control and Data Acquisition (SCADA) systems, when integrated with emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data analytics, can enhance the reliability, efficiency, and sustainability of energy systems. Cogeneration, which simultaneously produces electricity and thermal energy, requires consistent operational monitoring to prevent system failures and energy losses. By employing predictive maintenance techniques, manufacturing facilities can shift from reactive or scheduled maintenance to condition-based approaches that minimize downtime and operational costs. The review also examines key challenges related to data management, cybersecurity, system integration, and workforce readiness. Furthermore, it highlights the potential of digital twins, cloud-based SCADA architectures, and self-healing maintenance systems in advancing smart factory initiatives. The study concludes with recommendations and future research directions for sustainable and intelligent industrial energy management.