Digital Twin for Well Integrity with Real Time Surveillance

2020 ◽  
Author(s):  
Jorel Lopes Anjos ◽  
Pedro Esteves Aranha ◽  
André Leibsohn Martins ◽  
Felipe Lima Oliveira ◽  
Clemente José Gonçalves ◽  
...  
Keyword(s):  
Real Time ◽  
Digital Twin ◽  
Well Integrity

Monitoring casing well integrity in real time using a digital twin

2020 ◽  
Vol 20 (2020) ◽  
pp. 449-450
Author(s):  
Clemente Gonçalves ◽  
Felipe Lima De Oliveira ◽  
Marcus Vinicius Duarte Ferreira ◽  
Eduardo da Silva Paranhos Sobrinho ◽  
Thiago Barbosa Da Silva ◽  
...  
Keyword(s):  
Real Time ◽  
Digital Twin ◽  
Well Integrity

scholarly journals Digital Twin: Origin to Future

2021 ◽  
Vol 4 (2) ◽  
pp. 36
Author(s):  
Maulshree Singh ◽  
Evert Fuenmayor ◽  
Eoin Hinchy ◽  
Yuansong Qiao ◽  
Niall Murray ◽  
...  
Keyword(s):  
Real Time ◽  
Remote Access ◽  
Physical Entity ◽  
Industrial Systems ◽  
Digital Twin ◽  
Planning Optimization ◽  
Different Types ◽  
Pros And Cons ◽  
Number Of Publications ◽  
Digital Shadow

Digital Twin (DT) refers to the virtual copy or model of any physical entity (physical twin) both of which are interconnected via exchange of data in real time. Conceptually, a DT mimics the state of its physical twin in real time and vice versa. Application of DT includes real-time monitoring, designing/planning, optimization, maintenance, remote access, etc. Its implementation is expected to grow exponentially in the coming decades. The advent of Industry 4.0 has brought complex industrial systems that are more autonomous, smart, and highly interconnected. These systems generate considerable amounts of data useful for several applications such as improving performance, predictive maintenance, training, etc. A sudden influx in the number of publications related to ‘Digital Twin’ has led to confusion between different terminologies related to the digitalization of industries. Another problem that has arisen due to the growing popularity of DT is a lack of consensus on the description of DT as well as so many different types of DT, which adds to the confusion. This paper intends to consolidate the different types of DT and different definitions of DT throughout the literature for easy identification of DT from the rest of the complimentary terms such as ‘product avatar’, ‘digital thread’, ‘digital model’, and ‘digital shadow’. The paper looks at the concept of DT since its inception to its predicted future to realize the value it can bring to certain sectors. Understanding the characteristics and types of DT while weighing its pros and cons is essential for any researcher, business, or sector before investing in the technology.

scholarly journals SHION: Towards An Interactive Digital Twin Supporting Shopfloor Operations on Real Time

2020 ◽  
pp. 1-1
Author(s):  
Francisco Jose Lacueva-Perez ◽  
Setia Hermawati ◽  
Pedro Amoraga ◽  
Ricardo Salillas-Martinez ◽  
Rafael Del Hoyo Alonso ◽  
...  
Keyword(s):  
Real Time ◽  
Digital Twin

Toward a Digital Twin for real-time geometry assurance in individualized production

CIRP Annals ◽  
2017 ◽  
Vol 66 (1) ◽  
pp. 137-140 ◽  
Author(s):  
Rikard Söderberg ◽  
Kristina Wärmefjord ◽  
Johan S. Carlson ◽  
Lars Lindkvist
Keyword(s):  
Real Time ◽  
Digital Twin ◽  
Individualized Production

TAD-Net: An approach for real-time action detection based on temporal convolution network and graph convolution network in digital twin shop-floor

Digital Twin ◽  
2021 ◽  
Vol 1 ◽  
pp. 10
Author(s):  
Qing Hong ◽  
Yifeng Sun ◽  
Tingyu Liu ◽  
Liang Fu ◽  
Yunfeng Xie
Keyword(s):  
Real Time ◽  
Sequence Data ◽  
Network Models ◽  
Human Action ◽  
Practical Significance ◽  
Shop Floor ◽  
Action Detection ◽  
Digital Twin ◽  
Actual Application ◽  
Production Processes

Background: Intelligent monitoring of human action in production is an important step to help standardize production processes and construct a digital twin shop-floor rapidly. Human action has a significant impact on the production safety and efficiency of a shop-floor, however, because of the high individual initiative of humans, it is difficult to realize real-time action detection in a digital twin shop-floor. Methods: We proposed a real-time detection approach for shop-floor production action. This approach used the sequence data of continuous human skeleton joints sequences as the input. We then reconstructed the Joint Classification-Regression Recurrent Neural Networks (JCR-RNN) based on Temporal Convolution Network (TCN) and Graph Convolution Network (GCN). We called this approach the Temporal Action Detection Net (TAD-Net), which realized real-time shop-floor production action detection. Results: The results of the verification experiment showed that our approach has achieved a high temporal positioning score, recognition speed, and accuracy when applied to the existing Online Action Detection (OAD) dataset and the Nanjing University of Science and Technology 3 Dimensions (NJUST3D) dataset. TAD-Net can meet the actual needs of the digital twin shop-floor. Conclusions: Our method has higher recognition accuracy, temporal positioning accuracy, and faster running speed than other mainstream network models, it can better meet actual application requirements, and has important research value and practical significance for standardizing shop-floor production processes, reducing production security risks, and contributing to the understanding of real-time production action.

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