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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:

Development of deep reinforcement learning-based fault diagnosis method for rotating machinery in nuclear power plants
Gensheng Qian, Jingquan Liu
Progress in Nuclear Energy (2022) Vol. 152, pp. 104401-104401
Closed Access | Times Cited: 40

Showing 1-25 of 40 citing articles:

Insights into modern machine learning approaches for bearing fault classification: A systematic literature review
Afzal Ahmed Soomro, Masdi Muhammad, Ainul Akmar Mokhtar, et al.
Results in Engineering (2024) Vol. 23, pp. 102700-102700
Open Access | Times Cited: 18
Robust fault diagnosis of quayside container crane gearbox based on 2D image representation in frequency domain and CNN
Jianqun Zhang, Qing Zhang, Xianrong Qin, et al.
Structural Health Monitoring (2023) Vol. 23, Iss. 1, pp. 324-342
Closed Access | Times Cited: 23
A deep reinforcement learning-based intelligent fault diagnosis framework for rolling bearings under imbalanced datasets
Yonghua Li, Yipeng Wang, Xing Zhao, et al.
Control Engineering Practice (2024) Vol. 145, pp. 105845-105845
Closed Access | Times Cited: 9
Imbalanced sample fault diagnosis method for rotating machinery in nuclear power plants based on deep convolutional conditional generative adversarial network
Zhichao Wang, Hong Xia, Jiyu Zhang, et al.
Nuclear Engineering and Technology (2023) Vol. 55, Iss. 6, pp. 2096-2106
Open Access | Times Cited: 20
Machine learning and deep learning for safety applications: Investigating the intellectual structure and the temporal evolution
Leonardo Leoni, Ahmad BahooToroody, Mohammad Mahdi Abaei, et al.
Safety Science (2023) Vol. 170, pp. 106363-106363
Open Access | Times Cited: 19
A fault diagnosis of nuclear power plant rotating machinery based on multi-sensor and deep residual neural network
Wenzhe Yin, Hong Xia, Zhichao Wang, et al.
Annals of Nuclear Energy (2023) Vol. 185, pp. 109700-109700
Closed Access | Times Cited: 18
A fault diagnosis method for nuclear power plant rotating machinery based on adaptive deep feature extraction and multiple support vector machines
Wenzhe Yin, Hong Xia, Xueying Huang, et al.
Progress in Nuclear Energy (2023) Vol. 164, pp. 104862-104862
Closed Access | Times Cited: 16
Application of artificial intelligence technologies and big data computing for nuclear power plants control: a review
Derjew Ayele Ejigu, Yanjie Tuo, Xiaojing Liu
Frontiers in Nuclear Engineering (2024) Vol. 3
Open Access | Times Cited: 6
AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review
Younes Zahraoui, Tarmo Korõtko, Argo Rosin, et al.
Sustainability (2024) Vol. 16, Iss. 12, pp. 4959-4959
Open Access | Times Cited: 6
Review on Fault Diagnosis and Fault-Tolerant Control Scheme for Robotic Manipulators: Recent Advances in Ai, Machine Learning, and Digital Twin
Md Muzakkir Quamar, Ali Nasir
(2024)
Open Access | Times Cited: 5
Data-driven machinery fault diagnosis: A comprehensive review
Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley, et al.
Neurocomputing (2025), pp. 129588-129588
Open Access
A fault diagnosis method for nuclear power plants rotating machinery based on deep learning under imbalanced samples
Wenzhe Yin, Hong Xia, Xueying Huang, et al.
Annals of Nuclear Energy (2024) Vol. 199, pp. 110340-110340
Closed Access | Times Cited: 4
Enhanced predictive modeling of rotating machinery remaining useful life by using separable convolution backbone networks
Li Zou, Cong Ma, Jun Hu, et al.
Applied Soft Computing (2024) Vol. 156, pp. 111493-111493
Closed Access | Times Cited: 4
A Survey on Fault Diagnosis of Rotating Machinery Based on Machine Learning
Qi Wang, Rui Huang, Jianbin Xiong, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 10, pp. 102001-102001
Closed Access | Times Cited: 4
Development of deep reinforcement learning-based fault diagnosis method for actuator faults in unmanned aerial vehicles
Majd Saied, N. Tahan, K. Chreif, et al.
The Aeronautical Journal (2025), pp. 1-17
Closed Access
Development of a non-intrusive ROM for 5 × 5 rod bundles of PWR using small sample data
Hao Qian, Guangliang Chen, Dong Liu, et al.
Annals of Nuclear Energy (2025) Vol. 217, pp. 111347-111347
Closed Access
Fault diagnosis in reactor coolant pump with an automatic CNN-based mixed model
Jianping Zhang, Jingyu Liang, Jie Liu
Progress in Nuclear Energy (2024) Vol. 175, pp. 105294-105294
Closed Access | Times Cited: 3
A numerical simulation enhanced multi-task integrated learning network for fault detection in rotation vector reducers
Hui Wang, Shuhui Wang, Ronggang Yang, et al.
Mechanical Systems and Signal Processing (2024) Vol. 217, pp. 111525-111525
Closed Access | Times Cited: 3
Deep Q‐learning recommender algorithm with update policy for a real steam turbine system
Mohammad Hossein Modirrousta, Mahdi Aliyari Shoorehdeli, Mostafa Yari, et al.
IET Collaborative Intelligent Manufacturing (2023) Vol. 5, Iss. 3
Open Access | Times Cited: 7
A review on methods and applications of artificial intelligence on Fault Detection and Diagnosis in nuclear power plants
Aicheng Gong, Zhongjian Qiao, Xihui Li, et al.
Progress in Nuclear Energy (2024) Vol. 177, pp. 105474-105474
Closed Access | Times Cited: 2
Match-reinforcement learning with time frequency selection for bearing fault diagnosis
Jiaxuan Wang, Dawei Gao, Yongsheng Zhu, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 12, pp. 125005-125005
Closed Access | Times Cited: 6
Application of machine learning and computer vision methods to determine the size of NPP equipment elements in difficult measurement conditions
Dmytro Belytskyi, Ruslan Yermolenko, Kostiantyn Petrenko, et al.
Naukovij žurnal «Tehnìka ta energetika» (2023) Vol. 14, Iss. 4, pp. 42-53
Open Access | Times Cited: 5
A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants
Yixuan Sun, Sami Khairy, Richard Vilim, et al.
arXiv (Cornell University) (2024)
Open Access | Times Cited: 1
Static performance prediction of long-pulse negative ion based neutral beam injection experiment
Yang Li, Chundong Hu, Yuanzhe Zhao, et al.
Plasma Physics and Controlled Fusion (2024) Vol. 66, Iss. 6, pp. 065008-065008
Closed Access | Times Cited: 1
A noise generative network to reduce the gap between simulation and measurement signals in mechanical fault diagnosis
Hui Wang, Shuhui Wang, Ronggang Yang, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 136, pp. 108917-108917
Closed Access | Times Cited: 1
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