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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:

An interpretable data augmentation scheme for machine fault diagnosis based on a sparsity-constrained generative adversarial network
Liang Ma, Yu Ding, Zili Wang, et al.
Expert Systems with Applications (2021) Vol. 182, pp. 115234-115234
Closed Access | Times Cited: 61

Showing 26-50 of 61 citing articles:

Data Augmentation via Randomized Wavelet Expansion and Its Application in Few-Shot Fault Diagnosis of Aviation Hydraulic Pumps
Minghang Zhao, Xuyun Fu, Yongjian Zhang, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 71, pp. 1-13
Closed Access | Times Cited: 28
Progressive generative adversarial network for generating high-dimensional and wide-frequency signals in intelligent fault diagnosis
Zhijun Ren, Kai Huang, Yongsheng Zhu, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108332-108332
Closed Access | Times Cited: 4
CE-FFGAN: A feature fusion generative adversarial network with deep embedded category information for limited sample fault diagnosis of rotating machinery under speed variation
Chen Yang, Hongkun Li, Shunxin Cao, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102605-102605
Closed Access | Times Cited: 4
Enhancing Reliability Through Interpretability: A Comprehensive Survey of Interpretable Intelligent Fault Diagnosis in Rotating Machinery
Gang Chen, Junlin Yuan, Yiyue Zhang, et al.
IEEE Access (2024) Vol. 12, pp. 103348-103379
Open Access | Times Cited: 4
Data-driven machinery fault diagnosis: A comprehensive review
Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley, et al.
Neurocomputing (2025), pp. 129588-129588
Open Access
Multi-fidelity sub-label-guided transfer network with physically interpretable synthetic datasets for rotor fault diagnosis
Dongmin Lee, J. Lee, Minseok Choi, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 148, pp. 110467-110467
Closed Access
Fault diagnosis of wind turbines with generative adversarial network-based oversampling method
Shuai Yang, Yifei Zhou, Xu Chen, et al.
Measurement Science and Technology (2022) Vol. 34, Iss. 4, pp. 044004-044004
Closed Access | Times Cited: 17
A Reliable Deep Learning Approach for Time-Varying Faults Identification: Spacecraft Reaction Wheel Case Study
Abd-Elsalam R. Abd-Elhay, Wael A. Murtada, Mohamed Youssef
IEEE Access (2022) Vol. 10, pp. 75495-75512
Open Access | Times Cited: 16
A Novel Data Augmentation and Composite Multiscale Network for Mechanical Fault Diagnosis
Yuan Wei, Zhijun Xiao, Shulin Liu, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-12
Closed Access | Times Cited: 10
A multi-sensor signals denoising framework for tool state monitoring based on UKF-CycleGAN
Xudong Wei, Xianli Liu, Caixu Yue, et al.
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110420-110420
Closed Access | Times Cited: 9
Diagnosis of unbalanced rolling bearing fault sample based on adaptive sparse contrative Auto-encoder and IGWO-USELM
Haiquan Song, Wengang Ma, Zhonghe Han, et al.
Measurement (2022) Vol. 198, pp. 111353-111353
Closed Access | Times Cited: 15
Sliced Wasserstein cycle consistency generative adversarial networks for fault data augmentation of an industrial robot
Ziqiang Pu, Diego Cabrera, Chuan Li, et al.
Expert Systems with Applications (2023) Vol. 222, pp. 119754-119754
Closed Access | Times Cited: 8
Small Sample Reliability Assessment With Online Time-Series Data Based on a Worm Wasserstein Generative Adversarial Network Learning Method
Bo Sun, Zeyu Wu, Qiang Feng, et al.
IEEE Transactions on Industrial Informatics (2022) Vol. 19, Iss. 2, pp. 1207-1216
Closed Access | Times Cited: 13
TFFNet: A robust approach with anti-noise and domain shift adaptation for intelligent fault diagnosis of rotating machinery
Pushkar Kawale, Sitesh Kumar Mishra, Piyush Shakya
Journal of Vibration and Control (2024)
Closed Access | Times Cited: 2
Sparse measure of bearing fault features based on Legendre wavelet multi-scale multi-mode Entropy
Xiaoyang Zheng, Yan Huang, Xin Yu, et al.
Computers & Electrical Engineering (2024) Vol. 116, pp. 109204-109204
Closed Access | Times Cited: 2
A meta transfer learning method for gearbox fault diagnosis with limited data
Daoming She, Zhichao Yang, Yudan Duan, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 8, pp. 086114-086114
Closed Access | Times Cited: 2
Few-shot fault identification of complex equipment via metric-based features capture GAN combining prior knowledge-augmented strategy
Shusen Dou, Fudong Li, Yuanhong Chang, et al.
Journal of Manufacturing Systems (2023) Vol. 71, pp. 238-256
Closed Access | Times Cited: 6
Trustworthy Artificial Intelligence Based on an Explicable Temporal Feature Network for Industrial Fault Diagnosis
Junwei Hu, Yong Zhang, Weigang Li, et al.
Cognitive Computation (2023) Vol. 16, Iss. 2, pp. 534-545
Closed Access | Times Cited: 5
A zero-sample intelligent fault diagnosis method for bearings based on category relationship model
Qibin Wang, Ni Liu, Junji Wang, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 130, pp. 107739-107739
Closed Access | Times Cited: 5
Random convolution layer: an auxiliary method to improve fault diagnosis performance
Zhiqian Zhao, Runchao Zhao, Yinghou Jiao
Journal of Intelligent Manufacturing (2024)
Closed Access | Times Cited: 1
Review of imbalanced fault diagnosis technology based on generative adversarial networks
Hualin Chen, Jianan Wei, Haisong Huang, et al.
Journal of Computational Design and Engineering (2024) Vol. 11, Iss. 5, pp. 99-124
Open Access | Times Cited: 1
Advanced Data Augmentation Techniques for Enhanced Fault Diagnosis in Industrial Centrifugal Pumps
Dong-Yun Kim, Akeem Bayo Kareem, Daryl Domingo, et al.
Journal of Sensor and Actuator Networks (2024) Vol. 13, Iss. 5, pp. 60-60
Open Access | Times Cited: 1
Current status, application, and challenges of the interpretability of generative adversarial network models
Su-Lin Wang, Chengqiang Zhao, Lingling Huang, et al.
Computational Intelligence (2022) Vol. 39, Iss. 2, pp. 283-314
Closed Access | Times Cited: 6
A few-shot sample augmentation algorithm based on SCAM and DEPS for pump fault diagnosis
Fengqian Zou, Shengtian Sang, Ming Jiang, et al.
ISA Transactions (2023) Vol. 142, pp. 445-453
Closed Access | Times Cited: 3
Frequency-learning generative network (FLGN) to generate vibration signals of variable lengths
Jin Uk Ko, Jinwook Lee, Taehun Kim, et al.
Expert Systems with Applications (2023) Vol. 227, pp. 120255-120255
Closed Access | Times Cited: 3

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