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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:

A systematic literature review of machine learning methods applied to predictive maintenance
Thyago Peres Carvalho, Fabrízzio Soares, Roberto Oliveira Vita, et al.
Computers & Industrial Engineering (2019) Vol. 137, pp. 106024-106024
Closed Access | Times Cited: 972

Showing 1-25 of 972 citing articles:

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
Forecasting: theory and practice
Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, et al.
International Journal of Forecasting (2022) Vol. 38, Iss. 3, pp. 705-871
Open Access | Times Cited: 545
Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0
Zeki Murat Çınar, Abubakar Abdussalam Nuhu, Qasim Zeeshan, et al.
Sustainability (2020) Vol. 12, Iss. 19, pp. 8211-8211
Open Access | Times Cited: 505
Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions
Raffaele Cioffi, Marta Travaglioni, Giuseppina Piscitelli, et al.
Sustainability (2020) Vol. 12, Iss. 2, pp. 492-492
Open Access | Times Cited: 496
The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions
Aline F.S. Borges, Fernando J.B. Laurindo, Mauro de Mesquita Spínola, et al.
International Journal of Information Management (2020) Vol. 57, pp. 102225-102225
Closed Access | Times Cited: 496
Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms
Jack C.P. Cheng, Weiwei Chen, Keyu Chen, et al.
Automation in Construction (2020) Vol. 112, pp. 103087-103087
Closed Access | Times Cited: 370
Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
Andreas Theissler, Judith Pérez-Velázquez, Marcel Kettelgerdes, et al.
Reliability Engineering & System Safety (2021) Vol. 215, pp. 107864-107864
Open Access | Times Cited: 300
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
Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning
Nicole M. Ralbovsky, Igor K. Lednev
Chemical Society Reviews (2020) Vol. 49, Iss. 20, pp. 7428-7453
Closed Access | Times Cited: 264
Simulation in industry 4.0: A state-of-the-art review
William de Paula Ferreira, Fabiano Armellini, Luis Antonio de Santa-Eulália
Computers & Industrial Engineering (2020) Vol. 149, pp. 106868-106868
Open Access | Times Cited: 246
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
A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics
Haidar Hosamo Hosamo, Paul Ragnar Svennevig, Kjeld Svidt, et al.
Energy and Buildings (2022) Vol. 261, pp. 111988-111988
Open Access | Times Cited: 175
Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry
Pier Francesco Orrù, Andrea Zoccheddu, Lorenzo Sassu, et al.
Sustainability (2020) Vol. 12, Iss. 11, pp. 4776-4776
Open Access | Times Cited: 156
Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing
Swee Leong Sing, C.N. Kuo, Cheng‐Ting Shih, et al.
Virtual and Physical Prototyping (2021) Vol. 16, Iss. 3, pp. 372-386
Closed Access | Times Cited: 139
Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review
Marta Fernandes, Juan M. Corchado, Goreti Marreiros
Applied Intelligence (2022) Vol. 52, Iss. 12, pp. 14246-14280
Open Access | Times Cited: 123
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
Artificial intelligence techniques for enabling Big Data services in distribution networks: A review
Sara Barja-Martinez, Mònica Aragüés‐Peñalba, Íngrid Munné‐Collado, et al.
Renewable and Sustainable Energy Reviews (2021) Vol. 150, pp. 111459-111459
Open Access | Times Cited: 114
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
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
Developing a blockchain framework for the automotive supply chain: A systematic review
Kotha Raj Kumar Reddy, Angappa Gunasekaran, P. Kalpana, et al.
Computers & Industrial Engineering (2021) Vol. 157, pp. 107334-107334
Open Access | Times Cited: 107
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
Knowledge-Based Fault Diagnosis in Industrial Internet of Things: A Survey
Yuanfang Chi, Yanjie Dong, Z. Jane Wang, et al.
IEEE Internet of Things Journal (2022) Vol. 9, Iss. 15, pp. 12886-12900
Closed Access | Times Cited: 87
To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts
Nessrine Omrani, Giorgia Rivieccio, Ugo Fiore, et al.
Technological Forecasting and Social Change (2022) Vol. 181, pp. 121763-121763
Closed Access | Times Cited: 87
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
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