Industrial IoT for Intelligent Steelmaking With Converter Mouth Flame Spectrum Information Processed by Deep Learning

Han, Yang; Zhang, Cai-Jun; Wang, Lu; Zhang, Yanchao

doi:10.1109/tii.2019.2948100

Cited by 47 publications

(20 citation statements)

References 25 publications

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“…The owner, the general contract for investigation and design and construction, the contractor, the supplier of materials and equipment, the supervisor, etc., either party is generally at fault or has no linked quality responsibility. Affect the quality of steel structure process [3][4].…”

Section: ) Total Quality Controlmentioning

confidence: 99%

Retracted: Internet of Things Health Detection System in Steel Structure Construction Management

et al. 2020

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In recent years, the development of computer network technology, especially the hot Internet of things, has brought great convenience and comfort to people's life. Steel structure is also more and more widely used in the field of construction in China. The purpose of this study is to explore the application of Internet of things health monitoring system in steel structure construction management. In this study, through the use of example analysis, comprehensive data analysis and other methods, combined with steel structure health monitoring system and construction process management analysis. The results show that the relative deviation of the data within 1-33 is mostly less than 10%, but the relative deviation of 34-40 suddenly increases to 60%, and the actual data suddenly decreases, resulting in the deviation of about 50%. It is concluded that the time series model established in this study can objectively reflect the change trend and law of strain data, has certain prediction accuracy, and has certain value significance for monitoring data analysis and construction management of steel building structures.

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Section: ) Total Quality Controlmentioning

confidence: 99%

Retracted: Internet of Things Health Detection System in Steel Structure Construction Management

et al. 2020

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“…A fog system for production management has been presented in [188] who use activity data to determine resource allocation locations to contribute to management of a production operation. Furthermore, product inspection, which is a common application of instrumentation systems in a factory, has been performed by [189,190] who utilize images and sensor data in a cloud based system to monitor product quality.…”

Section: Smart Industrymentioning

confidence: 99%

“…RF [190] Classification-Bad or good product quality Heterogeneous (Various sensors from a production floor in a factory) CNN [189] Classification [192] Classification-Abnormal or normal vibration data (from electric motor in a crane) Homogeneous (Accelerometer) RF + SVM [193] Classification-Failure prediction Heterogeneous (Multiple sensors from SECOM dataset) RNN (LSTM) [194] Regression-Predicting data from sensors Heterogeneous (Different sensors [Pressure, Temperature, Vibration etc. ])…”

Section: Smart Industrymentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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“…After decades of application of advanced sensing, communication technology and distributed control system (DCS), most large steel mills have formed a five-level automation and information architecture that includes basic automation system, process control system, manufacturing execution system, manufacturing management system, and business decision-making system. The newly rising industrial internet of things (IIoT) and cyber physical system (CPS) technologies have further broken the barriers between different information systems and promoted the collection, fusion and storage of multi-source heterogeneous data (Cao et al, 2020;Han et al, 2020;. The IIoT platform and development of data science enable the widespread application of data-driven process monitoring methods (Nkonyana et al, 2019), in which normal operation conditions are modelled with historical process data and the state of the monitored process is then examined by evaluating the deviation of indicators Quiñones-Grueiro et al, 2019;.…”

Section: Introductionmentioning

confidence: 99%

Dynamic multimode process monitoring using recursive GMM and KPCA in a hot rolling mill process

Peng

Huang

Wang

2021

Systems Science & Control Engineering

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The increasing competitive market has put forward higher demand for iron and steel production process, which is characterized by high-dimensional, nonlinear and multi-scale coupling. The newly rising internet of things (IoT) and advanced communication technologies have promoted the widespread application of data-driven process monitoring methods. To deal with the multimode and non-stationary properties of hot rolling production, a dynamic multimode process monitoring method is proposed based on the recursive Gaussian mixture model (RGMM) and recursive kernel principal component analysis (RKPCA). The proposed approach is applied to the monitoring of the hot-rolled strip thickness oversizing, and comparative experiments are conducted with KPCA, GMM-KPCA on actual production data. Results show that the proposed method shows better performance than conventional methods in terms of fault detection rate and false alarm rate when detecting timevarying multimode faults. The proposed method has also been integrated into an actual system and has been running smoothly in large steel mills in China.

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Industrial IoT for Intelligent Steelmaking With Converter Mouth Flame Spectrum Information Processed by Deep Learning

Cited by 47 publications

References 25 publications

Retracted: Internet of Things Health Detection System in Steel Structure Construction Management

Retracted: Internet of Things Health Detection System in Steel Structure Construction Management

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Dynamic multimode process monitoring using recursive GMM and KPCA in a hot rolling mill process

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