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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:

Predictive maintenance in the Industry 4.0: A systematic literature review
Tiago Zonta, Cristiano André da Costa, Rodrigo da Rosa Righi, et al.
Computers & Industrial Engineering (2020) Vol. 150, pp. 106889-106889
Closed Access | Times Cited: 731

Showing 1-25 of 731 citing articles:

Industry 5.0: A survey on enabling technologies and potential applications
Praveen Kumar Reddy Maddikunta, Quoc‐Viet Pham, B. Prabadevi, et al.
Journal of Industrial Information Integration (2021) Vol. 26, pp. 100257-100257
Open Access | Times Cited: 1243
Predictive Maintenance and Intelligent Sensors in Smart Factory: Review
Martin Pech, Jaroslav Vrchota, J. Bednář
Sensors (2021) Vol. 21, Iss. 4, pp. 1470-1470
Open Access | Times Cited: 265
Industry 4.0 ten years on: A bibliometric and systematic review of concepts, sustainability value drivers, and success determinants
Morteza Ghobakhloo, Masood Fathi, Mohammad Iranmanesh, et al.
Journal of Cleaner Production (2021) Vol. 302, pp. 127052-127052
Closed Access | Times Cited: 247
Internet of things for smart factories in industry 4.0, a review
Mohsen Soori, Behrooz Arezoo, Roza Dastres
Internet of Things and Cyber-Physical Systems (2023) Vol. 3, pp. 192-204
Open Access | Times Cited: 238
On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
Mounia Achouch, Mariya Dimitrova, Khaled Ziane, et al.
Applied Sciences (2022) Vol. 12, Iss. 16, pp. 8081-8081
Open Access | Times Cited: 199
Machine learning and deep learning based predictive quality in manufacturing: a systematic review
Hasan Tercan, Tobias Meisen
Journal of Intelligent Manufacturing (2022) Vol. 33, Iss. 7, pp. 1879-1905
Open Access | Times Cited: 173
The Duo of Artificial Intelligence and Big Data for Industry 4.0: Applications, Techniques, Challenges, and Future Research Directions
Senthil Kumar Jagatheesaperumal, Mohamed Rahouti, Kashif Ahmad, et al.
IEEE Internet of Things Journal (2021) Vol. 9, Iss. 15, pp. 12861-12885
Closed Access | Times Cited: 146
Integration of Industry 4.0 technologies into Total Productive Maintenance practices
Guilherme Luz Tortorella, Flávio Sanson Fogliatto, Paulo Augusto Cauchick-Miguel, et al.
International Journal of Production Economics (2021) Vol. 240, pp. 108224-108224
Closed Access | Times Cited: 120
Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review
Joerg Leukel, Julián Gil González, Martin Riekert
Journal of Manufacturing Systems (2021) Vol. 61, pp. 87-96
Closed Access | Times Cited: 112
Overview of predictive maintenance based on digital twin technology
Zhong Dong, Zhelei Xia, Yi‐An Zhu, et al.
Heliyon (2023) Vol. 9, Iss. 4, pp. e14534-e14534
Open Access | Times Cited: 110
Challenges in predictive maintenance – A review
Pedro Nunes, José Santos, Eugénio M. Rocha
CIRP journal of manufacturing science and technology (2022) Vol. 40, pp. 53-67
Open Access | Times Cited: 108
Big data driven innovation for sustaining SME supply chain operation in post COVID-19 scenario: Moderating role of SME technology leadership
Sheshadri Chatterjee, Ranjan Chaudhuri, Mahmood Shah, et al.
Computers & Industrial Engineering (2022) Vol. 168, pp. 108058-108058
Open Access | Times Cited: 103
A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment
Mahboob Elahi, Samuel Olaiya Afolaranmi, José L. Martínez Lastra, et al.
Discover Artificial Intelligence (2023) Vol. 3, Iss. 1
Open Access | Times Cited: 101
Maintenance optimization in industry 4.0
Luca Pinciroli, Piero Baraldi, Enrico Zio
Reliability Engineering & System Safety (2023) Vol. 234, pp. 109204-109204
Open Access | Times Cited: 90
Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing
Changchun Liu, Haihua Zhu, Dunbing Tang, et al.
Robotics and Computer-Integrated Manufacturing (2022) Vol. 77, pp. 102357-102357
Closed Access | Times Cited: 83
Intelligent manufacturing execution systems: A systematic review
Ardeshir Shojaeinasab, Todd Charter, Masoud Jalayer, et al.
Journal of Manufacturing Systems (2022) Vol. 62, pp. 503-522
Closed Access | Times Cited: 81
Construction 4.0, Industry 4.0, and Building Information Modeling (BIM) for Sustainable Building Development within the Smart City
Yali Chen, Dan Huang, Zhen Liu, et al.
Sustainability (2022) Vol. 14, Iss. 16, pp. 10028-10028
Open Access | Times Cited: 81
Artificial neural network-based decision support systems in manufacturing processes: A systematic literature review
Fredrick Mumali
Computers & Industrial Engineering (2022) Vol. 165, pp. 107964-107964
Closed Access | Times Cited: 74
A predictive maintenance model for optimizing production schedule using deep neural networks
Tiago Zonta, Cristiano André da Costa, Felipe André Zeiser, et al.
Journal of Manufacturing Systems (2022) Vol. 62, pp. 450-462
Closed Access | Times Cited: 69
From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry
Marek Molęda, Bożena Małysiak-Mrozek, Weiping Ding, et al.
Sensors (2023) Vol. 23, Iss. 13, pp. 5970-5970
Open Access | Times Cited: 69
Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework
Foivos Psarommatis, Gökan May, Victor Azamfirei
Journal of Manufacturing Systems (2023) Vol. 68, pp. 376-399
Open Access | Times Cited: 56
Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends
Ayşegül Uçar, Mehmet Karaköse, Necim Kırımça
Applied Sciences (2024) Vol. 14, Iss. 2, pp. 898-898
Open Access | Times Cited: 55
An Artificial Intelligence Approach for Improving Maintenance to Supervise Machine Failures and Support Their Repair
Izabela Rojek, Małgorzata Jasiulewicz–Kaczmarek, Mariusz Piechowski, et al.
Applied Sciences (2023) Vol. 13, Iss. 8, pp. 4971-4971
Open Access | Times Cited: 54
Improved Fault Classification for Predictive Maintenance in Industrial IoT Based on AutoML: A Case Study of Ball-Bearing Faults
Russul H. Hadi, Haider Najy Hady, Ahmed Mudheher Hasan, et al.
Processes (2023) Vol. 11, Iss. 5, pp. 1507-1507
Open Access | Times Cited: 51
A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0
Mehdi Saman Azari, Francesco Flammini, Stefania Santini, et al.
IEEE Access (2023) Vol. 11, pp. 12887-12910
Open Access | Times Cited: 42
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