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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:

Deep reinforcement learning for condition-based maintenance planning of multi-component systems under dependent competing risks
Nailong Zhang, Wujun Si
Reliability Engineering & System Safety (2020) Vol. 203, pp. 107094-107094
Closed Access | Times Cited: 104

Showing 1-25 of 104 citing articles:

Machine learning for reliability engineering and safety applications: Review of current status and future opportunities
Zhaoyi Xu, Joseph H. Saleh
Reliability Engineering & System Safety (2021) Vol. 211, pp. 107530-107530
Open Access | Times Cited: 319
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
Deep reinforcement learning in production systems: a systematic literature review
Marcel Panzer, Benedict Bender
International Journal of Production Research (2021) Vol. 60, Iss. 13, pp. 4316-4341
Open Access | Times Cited: 145
Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics
Juseong Lee, Mihaela Mitici
Reliability Engineering & System Safety (2022) Vol. 230, pp. 108908-108908
Open Access | Times Cited: 111
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
A diagnostic method of freight wagons hunting performance based on wayside hunting detection system
Qunsheng Wang, Dadi Li, Jing Zeng, et al.
Measurement (2024) Vol. 227, pp. 114274-114274
Closed Access | Times Cited: 21
Condition-based maintenance via Markov decision processes: A review
Xiujie Zhao, Piao Chen, Loon Ching Tang
Frontiers of Engineering Management (2025)
Closed Access | Times Cited: 1
Optimal condition-based preventive maintenance policy for balanced systems
Jingjing Wang, Qingan Qiu, Huanhuan Wang, et al.
Reliability Engineering & System Safety (2021) Vol. 211, pp. 107606-107606
Closed Access | Times Cited: 56
Maintenance optimisation of multicomponent systems using hierarchical coordinated reinforcement learning
Yifan Zhou, Bangcheng Li, Tian Ran Lin
Reliability Engineering & System Safety (2021) Vol. 217, pp. 108078-108078
Closed Access | Times Cited: 55
A condition-based maintenance policy for continuously monitored multi-component systems with economic and stochastic dependence
Jordan L. Oakley, Kevin J. Wilson, Pete Philipson
Reliability Engineering & System Safety (2022) Vol. 222, pp. 108321-108321
Open Access | Times Cited: 51
A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning
Lin Fan, Huai Su, Wei Wang, et al.
Reliability Engineering & System Safety (2022) Vol. 225, pp. 108613-108613
Closed Access | Times Cited: 51
A deep reinforcement learning approach for rail renewal and maintenance planning
Reza Karami Mohammadi, Qing He
Reliability Engineering & System Safety (2022) Vol. 225, pp. 108615-108615
Closed Access | Times Cited: 48
Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems
Van-Thai Nguyen, Phuc Do, Alexandre Vosin, et al.
Reliability Engineering & System Safety (2022) Vol. 228, pp. 108757-108757
Open Access | Times Cited: 48
Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study
Kamyar Azar, Zohreh Hajiakhondi-Meybodi, Farnoosh Naderkhani
Reliability Engineering & System Safety (2022) Vol. 222, pp. 108405-108405
Closed Access | Times Cited: 42
Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization
Oluwaseyi Ogunfowora, Homayoun Najjaran
Journal of Manufacturing Systems (2023) Vol. 70, pp. 244-263
Open Access | Times Cited: 39
Sustainable Life-Cycle Maintenance Policymaking for Network-Level Deteriorating Bridges with a Convolutional Autoencoder–Structured Reinforcement Learning Agent
Xiaoming Lei, You Dong, Dan M. Frangopol
Journal of Bridge Engineering (2023) Vol. 28, Iss. 9
Closed Access | Times Cited: 32
Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning
Pablo G. Morato, C.P. Andriotis, Konstantinos G. Papakonstantinou, et al.
Reliability Engineering & System Safety (2023) Vol. 235, pp. 109144-109144
Open Access | Times Cited: 28
Reinforcement learning for predictive maintenance: a systematic technical review
Rajesh Siraskar, Satish Kumar, Shruti Patil, et al.
Artificial Intelligence Review (2023) Vol. 56, Iss. 11, pp. 12885-12947
Closed Access | Times Cited: 28
Applications of Reinforcement Learning for maintenance of engineering systems: A review
Alberto Pliego Marugán
Advances in Engineering Software (2023) Vol. 183, pp. 103487-103487
Closed Access | Times Cited: 27
Multi-agent deep reinforcement learning-based maintenance optimization for multi-dependent component systems
Phuc Do, Van-Thai Nguyen, Alexandre Voisin, et al.
Expert Systems with Applications (2024) Vol. 245, pp. 123144-123144
Open Access | Times Cited: 11
Health management review for fuel cells: Focus on action phase
Jian Zuo, Nadia Yousfi Steiner, Zhongliang Li, et al.
Renewable and Sustainable Energy Reviews (2024) Vol. 201, pp. 114613-114613
Open Access | Times Cited: 11
Synergetic-informed deep reinforcement learning for sustainable management of transportation networks with large action spaces
Li Lai, You Dong, C.P. Andriotis, et al.
Automation in Construction (2024) Vol. 160, pp. 105302-105302
Closed Access | Times Cited: 9
A deep reinforcement learning-driven multi-objective optimization and its applications on aero-engine maintenance strategy
Zeqi Wei, Zhibin Zhao, Zheng Zhou, et al.
Journal of Manufacturing Systems (2024) Vol. 74, pp. 316-328
Closed Access | Times Cited: 9
Optimizing Predictive Maintenance With Machine Learning for Reliability Improvement
Yali Ren
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering (2021) Vol. 7, Iss. 3
Closed Access | Times Cited: 42
A deep reinforcement learning framework for life-cycle maintenance planning of regional deteriorating bridges using inspection data
Xiaoming Lei, Ye Xia, Lu Deng, et al.
Structural and Multidisciplinary Optimization (2022) Vol. 65, Iss. 5
Closed Access | Times Cited: 36
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