Adaptive PLC Scan-Time Optimization in a Unified PLC–SCADA Architecture

Yuda Bakti Zainal

doi:10.33019/

Authors

Yuda Bakti Zainal Department of Electrical Engineering, Universitas Jenderal Achmad Yani

DOI:

https://doi.org/10.33019/

Keywords:

Digital Twin, Industrial Internet of Things (IIoT), PLC, Predictive Maintenance, SCADA

Abstract

Industrial automation systems require integrated intelligence to support Industry 4.0 manufacturing. However, PLC, IoT, predictive maintenance, and digital twin technologies are typically implemented independently, limiting system performance. This study proposes a unified adaptive PLC–SCADA architecture integrating adaptive PLC scan-time optimization, IoT monitoring, machine learning-based predictive maintenance, and digital twin synchronization within a cyber–physical framework. The proposed system was validated through a 14-day laboratory experiment using 40,320 time-stamped observations. Performance was evaluated in terms of energy consumption, PLC scan time, communication latency, and predictive maintenance accuracy. Compared with a conventional PLC architecture, the proposed framework reduced energy consumption by 25%, PLC scan time by 60.4%, and communication latency by 20.1%, while achieving 95.0% predictive maintenance accuracy. These results demonstrate the effectiveness of the proposed architecture for energy-efficient, intelligent, and data-driven industrial automation.

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