Evolution and Monitoring of Industrial Automation Using Flow Control Loop With Low-Cost Embedded Platform

Abstract

The flow control loop in industrial automation employs a low-cost embedded platform to improve system performance and enable real-time monitoring. The challenge is to develop an effective flow control loop for industrial automation using a low-cost embedded platform to improve system evolution and enable real-time monitoring. The goal is to develop a flow control loop for industrial automation that facilitates system evolution and real-time monitoring through an affordable embedded platform. Multi-scale Median Filtering (MSMF) is applied in pre-processing to remove noise and improve signal clarity, optimizing the flow control loop for monitoring and managing industrial automation on a low-cost embedded platform. SDN is applied in implementation strategies to improve flexibility, scalability, and communication efficiency in low-cost embedded platforms for industrial automation. In implementation strategies for low-cost embedded platforms in industrial automation, NFV improves flexibility and scalability by separating system functions from the hardware. Graph Convolutional Networks (GCN) are utilized in implementation strategies for low-cost embedded platforms to process spatial and temporal data, improving decision-making and control within industrial automation systems. The findings of the flow control loop for industrial automation with a low-cost embedded platform highlight enhanced efficiency, affordability, and real-time monitoring, leading to better system performance and reliability. The result shows that the proposed technique outperforms all, with accuracy at 98%, precision at 95%, recall at 89%, and F1-score at 90%, implemented using Python software. The future scope of the flow control loop for industrial automation on a low-cost embedded platform involves enhancing scalability, integrating advanced sensors, and optimizing system performance for a wider range of industrial applications.