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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:

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

Showing 20 citing articles:

Application of deep learning to fault diagnosis of rotating machineries
Hao Su, Ling Xiang, Aijun Hu
Measurement Science and Technology (2024) Vol. 35, Iss. 4, pp. 042003-042003
Open Access | Times Cited: 15
Intelligent fault diagnosis for air handing units based on improved generative adversarial network and deep reinforcement learning
Ke Yan, Cheng Lu, Xiang Ma, et al.
Expert Systems with Applications (2023) Vol. 240, pp. 122545-122545
Closed Access | Times Cited: 17
Fault diagnosis of nuclear power plant sliding bearing-rotor systems using deep convolutional generative adversarial networks
Qi Li, Weiwei Zhang, Feiyu Chen, et al.
Nuclear Engineering and Technology (2024) Vol. 56, Iss. 8, pp. 2958-2973
Open Access | Times Cited: 6
Improved versatility and robustness of bearing fault detection and diagnostic method for nuclear power plant
Jin Siang Yap, M. H. Lim, Leong M. Salman
Nuclear Engineering and Design (2024) Vol. 428, pp. 113474-113474
Closed Access | Times Cited: 5
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
Imbalanced data fault diagnosis method for nuclear power plants based on convolutional variational autoencoding Wasserstein generative adversarial network and random forest
Jun Guo, Yulong Wang, Sun Xiang, et al.
Nuclear Engineering and Technology (2024) Vol. 56, Iss. 12, pp. 5055-5067
Open Access | Times Cited: 3
A Novel Reinforcement Learning Agent for Rotating Machinery Fault Diagnosis with Data Augmentation
Zhenning Li, Hongkai Jiang, Xin Wang
Reliability Engineering & System Safety (2024) Vol. 253, pp. 110570-110570
Closed Access | Times Cited: 3
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
Conditional generative adversarial network based data augmentation for fault diagnosis of diesel engines applied with infrared thermography and deep convolutional neural network
Rongcai Wang, Xisheng Jia, Zichang Liu, et al.
Eksploatacja i Niezawodnosc - Maintenance and Reliability (2023) Vol. 26, Iss. 1
Open Access | Times Cited: 5
Self-improving few-shot fault diagnosis for nuclear power plant based on man-machine collaboration
Guolong Li, Yanjun Li, Site Li, et al.
Nuclear Engineering and Design (2024) Vol. 420, pp. 113051-113051
Closed Access | Times Cited: 1
Simulation data-driven fault diagnosis method for metro traction motor bearings under small samples and missing fault samples
Kailin Bi, Aihua Liao, Dingyu Hu, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 10, pp. 105117-105117
Closed Access | Times Cited: 1
A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps
Yingqian Liu, Qian Huang, Huairui Li, et al.
Applied System Innovation (2024) Vol. 7, Iss. 4, pp. 61-61
Open Access | Times Cited: 1
An intelligent detection approach for multi-part cover based on deep learning under unbalanced and small size samples
Lerui Chen, Yuk Ming Tang, Yidan Ma, et al.
Structural Health Monitoring (2024)
Closed Access | Times Cited: 1
MITDCNN: A multi-modal input Transformer-based deep convolutional neural network for misfire signal detection in high-noise diesel engines
Wenjie Li, Xiangpeng Liu, D.H. Wang, et al.
Expert Systems with Applications (2023) Vol. 238, pp. 121797-121797
Closed Access | Times Cited: 2
Improved Versatility and Robustness of Bearing Fault Detection and Diagnostic Method for Nuclear Power Plant
Jee Siang Yap, M. H. Lim, Leong M. Salman
(2024)
Closed Access
Incorporating Segmentive & Augmentive Tools in Cosine KNN for Bearing Intelligent Fault Diagnosis
J. S. Yap, M. H. Lim, M. Salman Leong
Journal of Vibration Engineering & Technologies (2024)
Closed Access
Dynamic Fractal Cluster Model of Informational Space Technological Process of Power Station
Pavlo Budanov, Yuliia Oliinyk, Artem Cherniuk, et al.
Lecture notes on data engineering and communications technologies (2024), pp. 141-155
Closed Access
Smart knob integrated by artificial intelligence–based green manufacturing model for sustainable environment
Zhangyi Zhao
The International Journal of Advanced Manufacturing Technology (2023)
Closed Access | Times Cited: 1
Adaptive data mining as a tool to predict mining machinery and equipment assembly life
V. A. Khramovskikh, A. N. Shevchenko, K. A. Nepomnyashchikh
Earth sciences and subsoil use (2023) Vol. 46, Iss. 2, pp. 212-225
Open Access
Data‐driven modelling for online fault pre‐warning in thermal power plant using incremental Gaussian mixture regression
Shengxiang Jin, Fengqi Si, Yunshan Dong, et al.
The Canadian Journal of Chemical Engineering (2023) Vol. 102, Iss. 4, pp. 1497-1508
Closed Access
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