Digital Twin Technology Utilizing Robots and Deep Learning

2020 ◽  
Author(s):  
Fuminori Yamasaki
Keyword(s):  
Deep Learning ◽  
Digital Twin

An integrated mixed reality system for safety-aware human-robot collaboration using deep learning and digital twin generation

2022 ◽  
Vol 73 ◽  
pp. 102258
Author(s):  
Sung Ho Choi ◽  
Kyeong-Beom Park ◽  
Dong Hyeon Roh ◽  
Jae Yeol Lee ◽  
Mustafa Mohammed ◽  
...  
Keyword(s):  
Deep Learning ◽  
Mixed Reality ◽  
Digital Twin ◽  
Human Robot Collaboration

A Digital Twin of Vertebral Body: Deep Learning Assisted Reconstruction and Simulating Biomechanics

2021 ◽  
Author(s):  
Hossein Ahmadian, Ph.D. ◽  
Prasath Mageswaren ◽  
Dukagjin Blakaj ◽  
Ehud Mendel ◽  
Soheil Soghrati ◽  
...  
Keyword(s):  
Deep Learning ◽  
Vertebral Body ◽  
Digital Twin

scholarly journals Deep learning enhanced digital twin for Closed-Loop In-Process quality improvement

CIRP Annals ◽  
2020 ◽  
Vol 69 (1) ◽  
pp. 369-372
Author(s):  
Pasquale Franciosa ◽  
Mikhail Sokolov ◽  
Sumit Sinha ◽  
Tianzhu Sun ◽  
Dariusz Ceglarek
Keyword(s):  
Quality Improvement ◽  
Deep Learning ◽  
Closed Loop ◽  
Process Quality ◽  
Digital Twin

scholarly journals Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing

2020 ◽  
Vol 2 (1) ◽  
pp. 34-36 ◽  
Author(s):  
Jay Lee ◽  
Moslem Azamfar ◽  
Jaskaran Singh ◽  
Shahin Siahpour
Keyword(s):  
Deep Learning ◽  
Cyber Physical Systems ◽  
Smart Manufacturing ◽  
Physical Systems ◽  
Digital Twin

Deep learning-enabled intelligent process planning for digital twin manufacturing cell

2020 ◽  
Vol 191 ◽  
pp. 105247 ◽  
Author(s):  
Chao Zhang ◽  
Guanghui Zhou ◽  
Junsheng Hu ◽  
Jing Li
Keyword(s):  
Deep Learning ◽  
Process Planning ◽  
Manufacturing Cell ◽  
Digital Twin ◽  
Intelligent Process

Digital Twin For Offshore Pipeline Corrosion Monitoring: A Deep Learning Approach

2021 ◽  
Author(s):  
Subrata Bhowmik
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Processing Time ◽  
Learning Algorithm ◽  
Corrosion Monitoring ◽  
Digital Twin ◽  
Pipeline Inspection ◽  
Deep Learning Algorithm ◽  
Inspection Data ◽  
Pipeline Corrosion

Abstract Pipeline corrosion is a major identified threat in the offshore oil and gas industry. In this paper, a novel computer vision-based digital twin concept for real-time corrosion inspection is proposed. The Convolution Neural Network (CNN) algorithm is used for the automated corrosion identification and classification from the ROV images and In-Line Inspection data. Predictive and prescriptive maintenance strategies are recommended based on the corrosion assessment through the digital twin. A Deep-learning Image processing model is developed based on the pipeline inspection images and In-Line Inspection images from some previous inspection data sets. During the corrosion monitoring through pipeline inspection, the digital twin system would be able to gather data and, at the same time, process and analyze the collected data. The analyzed data can be used to classify the corrosion type and determine the actions to be taken (develop predictive and prescriptive maintenance strategy). Convolution Neural Network, a well known Deep Learning algorithm, is used in the Tensorflow framework with Keras in the backend is used in the digital twin for corrosion inspection. CNN algorithm will first detect the corrosion and then the type of corrosion based on image classification. The deep-learning network training is done using 4000 images taken from the inspection video frames from a subsea pipeline inspection using ROV. The performances of both the methods are compared based on result accuracy as well as processing time. Deep Learning algorithm, CNN has approximately 81% accuracy for correctly identifying the corrosion and classify them based on severity through image classification. The processing time for the deep-learning method is significantly faster, and the digital twin generates the predictive or prescriptive strategy based on the inspection result in real-time. Deep-learning based digital twin for Corrosion inspection significantly improve current corrosion identification and reduce the overall time for offshore inspection. The inspection data loss due to the communication interference during real-time assessment can be eliminated using the digital twin. The image data can recover the required features based on other features available through the previous inspection. Furthermore, the system can adapt to the unrefined environment, making the proposed system robust and useful for other detection applications. The digital twin makes a recommended decision based on an expert system database during the real-time inspection. The complete corrosion monitoring process is performed in real-time on a cloud-based digital twin. The proposed pipeline corrosion inspection digital twin based on the CNN method will significantly reduce the overall maintenance cost and improve the efficiency of the corrosion monitoring system.

scholarly journals Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

2019 ◽  
Vol 18 (10) ◽  
pp. 4692-4707 ◽  
Author(s):  
Rui Dong ◽  
Changyang She ◽  
Wibowo Hardjawana ◽  
Yonghui Li ◽  
Branka Vucetic
Keyword(s):  
Deep Learning ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computing Systems ◽  
Digital Twin

scholarly journals A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiajie Jiang ◽  
Hui Li ◽  
Zhiwei Mao ◽  
Fengchun Liu ◽  
Jinjie Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Physical Model ◽  
Attention Mechanism ◽  
Maintenance Cost ◽  
Attention Network ◽  
Digital Twin ◽  
Mechanism Model ◽  
Fault Information

AbstractCondition monitoring and fault diagnosis of diesel engines are of great significance for safety production and maintenance cost control. The digital twin method based on data-driven and physical model fusion has attracted more and more attention. However, the existing methods lack deeper integration and optimization facing complex physical systems. Most of the algorithms based on deep learning transform the data into the substitution of the physical model. The lack of interpretability of the deep learning diagnosis model limits its practical application. The attention mechanism is gradually developed to access interpretability. In this study, a digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis is proposed with considering its signal characteristics of strong angle domain correlation and transient non-stationary, in which a new soft threshold filter is designed to draw more attention to multi decentralized local fault information dynamically in real time. Based on this attention mechanism, the distribution of fault information in the original signal can be better visualized to help explain the fault mechanism. The valve failure experiment on a diesel engine test rig is conducted, of which the results show that the proposed adaptive sparse attention mechanism model has better training efficiency and clearer interpretability on the premise of maintaining performance.

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