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With the continuous development of intelligent manufacturing technology, the intelligent production line for the Industrial Internet of Things occupies an important position in the field of industrial intelligence. OPC UA is a standard for communication data exchange between intelligent production line devices. OPC UA can establish a unified inform...
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REAL-TIME control of the process is presented in this paper. Utilizing SCADA and MATHWORKS RSLINX-OPC Toolbox through programmable logic controllers. RSLinx Remote RSLINX-OPC server from Rockwell Automation works on servers that have a client architecture, and it uses an RSLINX-OPC DDE server. The Clients in this situation are SCADA and MATHWORKS. There is a three-phase design and implementation flow. Phase one involved designing PLC-based applications, followed by developing SCADA applications and integrating these with processes through the RSLINX-OPC server. Simultaneously accessing process data in real time is done with SIMULINK-MATHWORKS models in MATHWORKS. MATHWORKS and MicroLogix 1400B PLCs have been used to validate the realization of the test system. Test results and process control suggest that REAL-TIME data is exchanged among SCADA, PLC, and MATHWORKS in an efficient and reliable manner. Monitoring and control of EV-to-Grid Load utilization by using PLC and SCADA at real-time data acquisition are performed in this research. Using this technology, you may connect all operational systems to an OPC server and exchange data in a highly efficient manner. Simulink is used to simulate the SIMULINK model (.mat file), PLC used to control and interface other electronics systems to MATLAB mode, and SCADA used for operational purposes as a screen; all three systems are networked or interfaced on RSLINX OPC-SERVER.
This book provides rigorous discussions, case studies, and recent developments in soft computing and its application in power systems enabled with power electronics-based equipment, biomedical engineering, and image processing. The readers would be benefitted from enhancing their knowledge and skills in the domain areas. This book also helps the readers in developing new and innovative ideas.
Over recent years, traditional manufacturing factories have been accelerating their transformation and upgrade toward smart factories, which are an important concept within Industry 4.0. As a key communication technology in the industrial internet architecture, time-sensitive networks (TSNs) can break through communication barriers between subsystems within smart factories and form a common network for various network flows. Traditional routing algorithms are not applicable for this novel type of network, as they cause unnecessary congestion and latency. Therefore, this study examined the classification of TSN flows in smart factories, converted the routing problem into two graphical problems, and proposed two heuristic optimization algorithms, namely GATTRP and AACO, to find the optimal solution. The experiments showed that the algorithms proposed in this paper could provide a more reasonable routing arrangement for various TSN flows with different time sensitivities. The algorithms could effectively reduce the overall delay by up to 74% and 41%, respectively, with promising operating performances.
