Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects-Reference-Cited by-同舟云学术

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Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects

Published:2022 Issue:4 Volume:24 Page:2255-2291
ISSN:1553-877X
Container-title:IEEE Communications Surveys & Tutorials
language:
Short-container-title:IEEE Commun. Surv. Tutorials

Author:

Mihai Stefan¹,Yaqoob Mahnoor¹^ORCID,Hung Dang V.²^ORCID,Davis William¹^ORCID,Towakel Praveer¹,Raza Mohsin³,Karamanoglu Mehmet¹^ORCID,Barn Balbir¹,Shetve Dattaprasad⁴,Prasad Raja V.⁴^ORCID,Venkataraman Hrishikesh⁴,Trestian Ramona¹^ORCID,Nguyen Huan X.¹^ORCID

Affiliation:

1. London Digital Twin Research Centre, Middlesex University, London, U.K

2. Department of Civil Engineering, National University of Civil Engineering, Hanoi, Vietnam

3. Department of Computer Science, Edge Hill University, Ormskirk, U.K

4. Indian Institute of Information Technology, Sri City, India

Funder

U.K.–India Education and Research Initiative

British Council’s Institutional Links through the Newton Programme Vietnam Partnership

British Council Indonesia’s Going Global Partnerships Programme

British Council was provided by the U.K. Department of Business, Energy and Industrial Strategy

VinGroup Innovation Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/9739/9956928/09899718.pdf?arnumber=9899718

Reference208 articles.

1. Fog computing and convolutional neural network enabled prognosis for machining process optimization

2. Method of Predictive Maintenance for Induction Furnace Based on Neural Network

3. A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin

4. An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples

5. Predicting remaining useful life using time series embeddings based on recurrent neural networks;gugulothu;arXiv 1709 01073,2017

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4. A meta-PINN framework for online operational monitoring of high-power induction furnace;Journal of Manufacturing Systems;2024-10

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