Assessing Industrial Communication Protocols to Bridge the Gap between Machine Tools and Software Monitoring

This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.

Section: Power Plant Control and Automationmentioning

confidence: 99%

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation

Buratto,

Muniz,

Nied

et al. 2024

“…The eighth paper [ 8 ] assesses industrial communication protocols to bridge the gap between machine tools and software monitoring. It presents an empirical study of three protocols: OPC-UA, Modbus, and Ethernet/IP.…”

Section: Summary Of the Special Issuementioning

confidence: 99%

Intelligent Industrial Process Control Systems

Grobelna

2023

“…In the future, the calculation of descriptive statistics will replace the hyperparameters used for sample screening, to adaptively determine the number of primary screening samples according to the distribution of the samples. In addition, how to achieve efficient information flow collaboration between cameras and image data directly, as well as information exchange on industrial sites, are also key issues that need to be addressed to truly implement defectmonitoring technology in the field, which is also our future research focus [45,46].…”

Section: Limitations and Future Workmentioning

confidence: 99%

A Novel ST-YOLO Network for Steel-Surface-Defect Detection

Ma,

Zhang,

Zhao

2023

Recent progress has been made in defect detection using methods based on deep learning, but there are still formidable obstacles. Defect images have rich semantic levels and diverse morphological features, and the model is dynamically changing due to ongoing learning. In response to these issues, this article proposes a shunt feature fusion model (ST-YOLO) for steel-defect detection, which uses a split feature network structure and a self-correcting transmission allocation method for training. The network structure is designed to specialize the process of classification and localization tasks for different computing needs. By using the self-correction criteria of adaptive sampling and dynamic label allocation, more sufficiently high-quality samples are utilized to adjust data distribution and optimize the training process. Our model achieved better performance on the NEU-DET datasets and the GC10-DET datasets and was validated to exhibit excellent performance.