Jul 2026· Journal of Electrical Systems and Information Technology· Vol 13· 0 citations· 34 references
Abstract
Supervisory Control and Data Acquisition (SCADA) systems have emerged as a highly effective technology for automating dynamic industrial and engineering processes. Globally, numerous automated systems are designed based on SCADA principles due to their capability to efficiently collect, archive, visualize, and transmit critical operational data. Modern Open-SCADA platforms enable rapid development of large-scale distributed systems by leveraging pre-built components, while also allowing customization through built-in tools and configurable settings. In parallel, Programmable Logic Controllers (PLCs) offer significant advantages for automated control applications, including simplicity of operation, high processing speed, reliability, noise immunity, and long-term stability. The versatility of PLCs has made them a cornerstone in a variety of sectors, ranging from industrial automation and energy production to engineering research and complex process control. Although PLCs have certain limitations, empirical evidence indicates that their benefits outweigh their drawbacks, making them suitable for both simple and sophisticated control systems. This paper presents a comprehensive review of SCADA and PLC technologies, emphasizing their applications in power distribution and industrial automation. The study details the architecture of SCADA systems, including the proposed control and monitoring scheme implemented in a real-time network. This scheme ensures continuous power availability through appropriately designed breaker interlocks at all levels of switchboards within substations. To enhance reliability, redundant Optical Fiber Communication (OFC) cables and Ethernet switches are integrated at every level of Remote Terminal Units (RTUs). Furthermore, a case study demonstrates the deployment of PLC and SCADA systems in a critical building automation scenario, highlighting practical applications and system performance. Additionally, the discussion integrates the perspective of Distributed Control Systems (DCS), illustrating how SCADA and PLC can complement DCS architectures to optimize process control, improve system resilience, and enable advanced monitoring applications in complex industrial environments. The paper further addresses the emerging domain of Electric Vehicle (EV) charging infrastructure integration, covering SCADA interfaces with EV charging networks, smart charging coordination, Vehicle-to-Grid (V2G) bidirectional power exchange, and AI-based load management strategies in distribution systems.
Digital Twin (DT) technology has become a key enabler of smart manufacturing; however, its application in Computer Numerical Control (CNC) systems remains largely limited to monitoring and predictive tasks rather than real-time control. This limitation is particularly critical for remote operation of multi-axis CNC machines, where latency and synchronization directly affect control performance. This paper presents a systematic review of Digital Twin architectures for CNC machine tools, focusing on their suitability for remote monitoring and control. A PRISMA-based methodology is adopted to ensure a transparent and reproducible study selection process. The selected works are analyzed from an architectural perspective, considering system structure, communication protocols, synchronization strategies, and control integration. The results indicate that most existing implementations are not designed for time-critical closed-loop control and exhibit significant latency constraints. Cloud-based architectures typically introduce delays ranging from tens to hundreds of milliseconds, whereas edge-based approaches reduce latency to a few milliseconds, highlighting a trade-off between scalability and real-time performance. The study identifies key research gaps and emphasizes the need for hybrid edge–cloud Digital Twin architectures capable of supporting reliable and low-latency remote CNC control.
Omar A. Hashem, A. M. Bassiuny, Hussein M. A. Hussein et al.· Evolutionary Intelligence· 0 citations
Legacy conveyor systems remain widely deployed across manufacturing industries but often lack the sensing, connectivity, and real-time monitoring capabilities required for alignment with Industry 4.0 objectives. As critical material-handling assets, their unexpected failures, particularly at the induction motor drive, result in unplanned downtime, production disruptions, and higher maintenance costs. While system monitoring frameworks have been extensively studied for advanced or newly designed equipment, their application to retrofitting legacy assets, such as conveyor systems, remains limited. This work presents a structured framework for modernizing legacy belt conveyor systems through the integration of sensing, control, and visualization technologies to enable real-time operational monitoring. The induction motor of the conveyor has been retrofitted with multiple sensors, including temperature, vibration, current, and voltage, providing comprehensive data acquisition on the system’s performance. The acquired data were processed through a programmable logic controller (PLC) and integrated into the Ignition supervisory control and data acquisition (SCADA) platform, where a digital dashboard has been developed to visualize live operational states, trigger alarms, and record historical trends. The primary contribution of this study is a modular brownfield retrofit workflow that integrates multimodal sensing with PLC-based signal processing and SCADA visualization. Experimental validation under real operating conditions confirmed the operational correctness and effectiveness of the proposed framework and modernized system. The study establishes a foundation for real-time monitoring of legacy belt conveyor systems and demonstrates an extensible path to modernize non-digital manufacturing assets.
S. Singh, Xiangyu Jiang, Tyler Hartley et al.· The International Journal of...· 0 citations
Port automation has progressed from standalone mechanical devices to interconnected, data-driven systems that support global trade. This paper examines how port automation has grown, focusing on equipment systems such as quay cranes, Automated Guided Vehicles (AGVs), and Automated Stacking Cranes (ASCs) and other automated yard equipment. Early improvements boosted lifting capacity and reduced manual effort, while newer approaches integrate artificial intelligence (AI), Internet of Things (IoT) sensors, and digital twin simulations to coordinate equipment in real time. These advances have raised efficiency, lowered emissions, and improved safety. However, some obstacles remain. Port managers must solve cooperation challenges, data protocol differences, and cybersecurity risks. Research indicates that advanced autonomy, including reinforcement learning, dynamic scheduling, and AI-based condition monitoring, will be important to solve these problems. The sector also needs to make standards, which ensure that equipment from different managers can communicate easily. Advances in electrification and energy management can help achieve sustainability targets, including reduced carbon footprints and optimized resource consumption. By examining a combined bibliometric analysis, qualitative content coding, and term-frequency analysis, this study reviews current progress and outlines future directions and emphasizes solutions that account for technical, organizational, and social factors. This approach may help modern ports remain adaptable, reliable, and resilient in a rapidly shifting maritime environment.
Yihan Liu, Rauno Heikkilä· European Transport Research...· 0 citations
High-resolution energy data is increasingly central to Industry 4.0, where electrical signals such as three-phase voltage and current carry rich information about machine condition, tool wear, and process dynamics. Capturing this information in practice remains difficult: commercial power analysis are largely proprietary, offer limited or no access to high-sampling rate data for transient analysis, restrict access to raw waveform data, and offer no customization, while general-purpose open hardware lacks the front-end accuracy, isolation, and robustness required for industrial measurement. This paper presents Autonomous Energy Monitoring System (AEMS), an open-source, low-cost, and modular platform supported by a host, edge-gateway, and optional cloud software stack that enables autonomous, long-duration acquisition independent of a continuously connected host and thereby closes this gap by combining research-grade fidelity with industrial deployability. The system acquires three-phase voltage and current through an isolated front-end and a 24-bit, simultaneously sampling analog-to-digital converter, managed by a dual-core architecture that separates deterministic acquisition and on-board logging from host communication and control. Industrial interfaces (Ethernet, RS-485/Modbus, and BLE) together with hardware-level synchronization enable scalable, time-aligned acquisition across multiple machines, supported by a complete host, edge-gateway, and optional cloud software stack. We validate the platform on a three-axis CNC machining center, where it resolves spindle, feed-drive, rapid-traverse, and material-removal energy states and detects feed-rate changes as small as 50 mm/min. By releasing the full hardware and firmware openly, this work aims to democratize access to high-fidelity energy monitoring for both researchers and small and medium-sized manufacturers.
Vignesh Selvaraj, A. Nagaraj, Shengyuan Zhang et al.· 0 citations
Switched reluctance motors (SRMs) are attracting increasing attention due to their operational advantages and their industrial use is rapidly growing. In parallel with advancements in motor technologies, there have also been significant developments in semiconductor technologies, control
systems and condition monitoring. Programmable logic controllers (PLCs), which play a key role in these areas, are increasingly used in industrial motor control and data acquisition processes due to their modularity, reliability and compatibility with modern human-machine interface (HMI) tools.
This study focuses on the design and implementation of a real-time laboratory platform for monitoring the electrical and mechanical behaviour of an 8/6 SRM using an advanced PLC. The proposed system visualises key parameters such as phase current and torque both locally through a HMI dashboard
and remotely via a web interface. In addition to real-time monitoring, the system also performs data archiving during the condition monitoring process, enabling the storage and retrospective analysis of operational data. This configuration provides accessibility, transparency and flexibility
in both academic and industrial settings. The study enables both local and web-based remote condition monitoring of SRM data, along with systematic data archiving, using an advanced PLC, which contributes to the literature and offers potential benefits in the field of industrial maintenance.
B. Gecer, N. F. Serteller, A. N. Akpolat et al.· Insight - Non-Destructive Te...· 0 citations
Abstract. The growing need of intelligent and adaptable manufacturing systems requires the design of adaptive control strategies that can work in the dynamism and uncertainties. Conventional forms of control such as fixed-gain PID controllers may not be effective to provide optimal control in a system that incorporates process variations, tool wear and external disturbances. The paper introduces a built-in artificial intelligence (AI) framework to adaptive control of automated manufacturing systems, which allows making decisions in real-time at the edge level. The proposed architecture will integrate sensor-driven data acquisition with a mini-AI unit to execute on an embedded system to dynamically adjust control points. The hybrid control method is developed and an introduction of AI-based corrective actions to the baseline controller target are to minimize the tracking error and increase the stability of the system. The framework is applicable to a representative manufacturing system, and the performance of the system is compared with the traditional control methods. Experimentally it has been demonstrated to possess superior response time, reduced steady-state error and high robustness to various operating conditions. The proposed solution offers a computationally efficient and scalable solution to intelligent manufacturing systems of the next generation.
V. B· Materials Research Proceedin...· 0 citations
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