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Internet of things for predictive maintenance optimization in SCADA-based industrial automation systems
Abstract
The rapid advancement of digital technologies has significantly influenced industrial automation, particularly with the integration of the Internet of Things (IoT) into Supervisory Control and Data Acquisition (SCADA) systems. SCADA systems play a critical role in monitoring and controlling industrial operations, such as manufacturing, energy distribution, and water management. However, managing large-scale operations requires efficient maintenance strategies, and predictive maintenance has emerged as a solution to anticipate equipment failures through real-time data. This research aims to explore the integration of IoT into SCADA systems to optimize predictive maintenance. The study uses a qualitative literature review approach to analyze current practices and challenges in implementing IoT-based predictive maintenance in industrial automation. The findings indicate that IoT integration significantly enhances SCADA systems by enabling real-time monitoring and predictive analytics, leading to reduced operational costs, improved efficiency, and extended equipment lifespan. However, challenges related to data security, interoperability, and infrastructure remain significant. The results of this study provide insights into the effectiveness and potential of IoT in predictive maintenance optimization for SCADA-based industrial systems.
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DOI: http://dx.doi.org/10.30811/jpl.v23i4.7220
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