Real-time Situation Awareness of Industrial Process based on Deep Learning at the Edge Server

Xu, Rongbin; Lin, Wangxing; Liu, Zhiqiang; Wang, Menglong; Lin, Yuanmo; Xie, Ying

doi:10.1109/ccgrid49817.2020.000-3

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…Since there will be huge amounts of data from sensors and video/surveillance cameras, it can be iltered and discovered for only useful data using edge analytics which cuts down the resources [25]. The AI-assisted edge platform can help in situational predictions or estimations and real-time analysis based on dynamic decision-making and dynamic resource allocation [26], [27].…”

Section: Situation Awarenessmentioning

confidence: 99%

Edge computing for critical environments: Vision and existing solutions

Ijaz Ahmad,

Andrea Gentili,

Rupender Singh

et al. 2023

ITU J-FET

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With the increasing connectivity of critical infrastructures through wireless technologies, the importance of edge computing is increasing manyfolds. Edge computing has become an important phenomenon combining the strengths of distributed computing technologies with those of telecommunication technologies. Therefore, new technological breakthroughs are happening in the realm of edge computing for critical environments, such as the next generation underground and open-pit mining. Since the mining technologies are moving at a faster pace towards digitization leveraging connectivity technologies, edge computing plays a crucial role in bringing computing and connectivity into mines to provide latency- and security-critical services on site. In this article, we study how edge computing fulfills the needs of critical environments, focusing on mining environments, and provides important insight into future research directions.

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Section: Situation Awarenessmentioning

confidence: 99%

Edge computing for critical environments: Vision and existing solutions

Ijaz Ahmad,

Andrea Gentili,

Rupender Singh

et al. 2023

ITU J-FET

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“…min Tr(F T L S F) (5) Here, L S is a Laplacian matrix defined as L S = D − S, where D is the diagonal matrix with the ith diagonal element…”

Section: Adaptive Local Structurementioning

confidence: 99%

“…However, in a production process, workers usually need to operate a master‐slave control system by adding force feedback, 4 where the applied force is set by the control system. Furthermore, the informatization of equipment due to the lack of continuous and real‐time interaction as well as fusion may result in the lack of communication among real time solution 5 . All these factors lead to poor intelligence of production management at initiative and predictive pattern required by a smart factory defined in Industrial 4.0.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the informatization of equipment due to the lack of continuous and real-time interaction as well as fusion may result in the lack of communication among real time solution. 5 All these factors lead to poor intelligence of production management at initiative and predictive pattern required by a smart factory defined in Industrial 4.0. It is expected to find an efficient and accurate method of SA to detect potential risks of failure of mechanical components at real time.…”

Section: Introductionmentioning

confidence: 99%

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Unified adaptive deep classification for industrial real‐time situation awareness in edge environment

Liu

Lin

et al. 2021

Concurrency and Computation

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The rapidly growing information technologies such as cloud computing, edge computing, artificial intelligence offer cutting-edge technical means for virtual reality of industrial manufacturing process. An efficient and accurate method for situation awareness (SA) to monitor the process of marginal layer plays an essential role to enhance the safety of industrial equipment. In this study, a manufacturing life cycle is described and characterized as operation and remote controlling of industrial machineries. The edge servers are first employed to track physical devices. Then a novel unified adaptive deep classification framework for SA with semi-supervised classification method is developed to find unknown patterns in a huge amount of industrial data. Furthermore, a robust model based on the selection of adaptive local neighbor is proposed to find out an optimal weight and high similarity of neighbors. The combination of the proposed framework and model can realize real-time SA of modern manufacturing the execution system. Plentiful experiments have been conducted to demonstrate the high accuracy and reliability of the proposed framework.

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Real-time Situation Awareness of Industrial Process based on Deep Learning at the Edge Server

Cited by 2 publications

References 6 publications

Edge computing for critical environments: Vision and existing solutions

Edge computing for critical environments: Vision and existing solutions

Unified adaptive deep classification for industrial real‐time situation awareness in edge environment

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