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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Data Super-Network Fault Prediction Model and Maintenance Strategy for Mechanical Product Based on Digital Twin
Zhifeng Liu, Wei Chen, Caixia Zhang, et al.
IEEE Access (2019) Vol. 7, pp. 177284-177296
Open Access | Times Cited: 58

Showing 1-25 of 58 citing articles:

Digital twin modeling
Fei Tao, Bin Xiao, Qinglin Qi, et al.
Journal of Manufacturing Systems (2022) Vol. 64, pp. 372-389
Closed Access | Times Cited: 565
Digital Twin for maintenance: A literature review
Itxaro Errandonea, Sergio Beltrán, Saioa Arrizabalaga
Computers in Industry (2020) Vol. 123, pp. 103316-103316
Closed Access | Times Cited: 515
The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities
M. Mazhar Rathore, Syed Attique Shah, Dhirendra Shukla, et al.
IEEE Access (2021) Vol. 9, pp. 32030-32052
Open Access | Times Cited: 339
Low-head pumped hydro storage: A review of applicable technologies for design, grid integration, control and modelling
Justus Hoffstaedt, Daan Truijen, Jonathan Fahlbeck, et al.
Renewable and Sustainable Energy Reviews (2022) Vol. 158, pp. 112119-112119
Open Access | Times Cited: 99
Digital twin applications in aviation industry: A review
Minglan Xiong, Huawei Wang
The International Journal of Advanced Manufacturing Technology (2022) Vol. 121, Iss. 9-10, pp. 5677-5692
Closed Access | Times Cited: 90
A novel digital twin approach based on deep multimodal information fusion for aero-engine fault diagnosis
Yufeng Huang, Jun Tao, Gang Sun, et al.
Energy (2023) Vol. 270, pp. 126894-126894
Closed Access | Times Cited: 53
The advance of digital twin for predictive maintenance: The role and function of machine learning
Chong Chen, Huibin Fu, Yu Zheng, et al.
Journal of Manufacturing Systems (2023) Vol. 71, pp. 581-594
Open Access | Times Cited: 44
A comprehensive survey on digital twin for future networks and emerging Internet of Things industry
Akram Hakiri, Aniruddha Gokhale, Sadok Ben Yahia, et al.
Computer Networks (2024) Vol. 244, pp. 110350-110350
Closed Access | Times Cited: 27
Digital twin–driven aero-engine intelligent predictive maintenance
Minglan Xiong, Huawei Wang, Qiang Fu, et al.
The International Journal of Advanced Manufacturing Technology (2021) Vol. 114, Iss. 11-12, pp. 3751-3761
Closed Access | Times Cited: 101
On the requirements of digital twin-driven autonomous maintenance
Samir Khan, Michael Farnsworth, Richard McWilliam, et al.
Annual Reviews in Control (2020) Vol. 50, pp. 13-28
Open Access | Times Cited: 84
Intelligent scheduling of a feature-process-machine tool supernetwork based on digital twin workshop
Zhifeng Liu, Wei Chen, Caixia Zhang, et al.
Journal of Manufacturing Systems (2020) Vol. 58, pp. 157-167
Closed Access | Times Cited: 82
Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review
Bin He, Long Liu, Dong Zhang
Journal of Computing and Information Science in Engineering (2021) Vol. 21, Iss. 3
Closed Access | Times Cited: 68
Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects
Georgios Falekas, Athanasios Karlis
Energies (2021) Vol. 14, Iss. 18, pp. 5933-5933
Open Access | Times Cited: 60
Advances of Digital Twins for Predictive Maintenance
Yingchao You, Chong Chen, Fu Hu, et al.
Procedia Computer Science (2022) Vol. 200, pp. 1471-1480
Open Access | Times Cited: 51
A Comprehensive Review of Digital Twin from the Perspective of Total Process: Data, Models, Networks and Applications
Honghai Wu, Pengwei Ji, Huahong Ma, et al.
Sensors (2023) Vol. 23, Iss. 19, pp. 8306-8306
Open Access | Times Cited: 27
A review of digital twin technology for electromechanical products: Evolution focus throughout key lifecycle phases
Zhexin Cui, Xiaolang Yang, Jiguang Yue, et al.
Journal of Manufacturing Systems (2023) Vol. 70, pp. 264-287
Closed Access | Times Cited: 23
Knowledge map and forecast of digital twin in the construction industry: State-of-the-art review using scientometric analysis
Haiyan Xie, Mengyang Xin, Caiwu Lu, et al.
Journal of Cleaner Production (2022) Vol. 383, pp. 135231-135231
Closed Access | Times Cited: 33
Opportunities and Challenges to Develop Digital Twins for Subsea Pipelines
Bai-Qiao Chen, Paulo Maurício Videiro, C. Guedes Soares
Journal of Marine Science and Engineering (2022) Vol. 10, Iss. 6, pp. 739-739
Open Access | Times Cited: 28
When is a simulation a digital twin? A systematic literature review
Ana Wooley, Daniel F. Silva, Julia Bitencourt
Manufacturing Letters (2023) Vol. 35, pp. 940-951
Open Access | Times Cited: 17
Maintenance optimization for a multi-unit system with digital twin simulation
Jyrki Savolainen, Michele Urbani
Journal of Intelligent Manufacturing (2021) Vol. 32, Iss. 7, pp. 1953-1973
Open Access | Times Cited: 40
Lifetime Prediction Using a Tribology-Aware, Deep Learning-Based Digital Twin of Ball Bearing-Like Tribosystems in Oil and Gas
Prathamesh S. Desai, Victoria Granja, C. Fred Higgs
Processes (2021) Vol. 9, Iss. 6, pp. 922-922
Open Access | Times Cited: 34
Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks
Jithin Jagannath, Keyvan Ramezanpour, Anu Jagannath
(2022), pp. 81-86
Closed Access | Times Cited: 26
The rapid construction method of the digital twin polymorphic model for discrete manufacturing workshop
Dongjie Zhang, Zhifeng Liu, Fuping Li, et al.
Robotics and Computer-Integrated Manufacturing (2023) Vol. 84, pp. 102600-102600
Closed Access | Times Cited: 15
Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices
Nur Haninie Abd Wahab, Khairunnisa Hasikin‬, Khin Wee Lai, et al.
PeerJ Computer Science (2024) Vol. 10, pp. e1943-e1943
Open Access | Times Cited: 5
Digital Twin applied to Predictive Maintenance for Industry 4.0
Rochdi Kerkeni, Safa Khlif, Anis Mhalla, et al.
Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems (2024) Vol. 7, Iss. 4
Closed Access | Times Cited: 5
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