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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:

Rolling Bearing Fault Diagnosis in Limited Data Scenarios Using Feature Enhanced Generative Adversarial Networks
Wenlong Fu, Xiaohui Jiang, Chao Tan, et al.
IEEE Sensors Journal (2022) Vol. 22, Iss. 9, pp. 8749-8759
Closed Access | Times Cited: 26

Showing 1-25 of 26 citing articles:

Rolling bearing fault diagnosis based on 2D time-frequency images and data augmentation technique
Wenlong Fu, Xiaohui Jiang, Bailin Li, et al.
Measurement Science and Technology (2022) Vol. 34, Iss. 4, pp. 045005-045005
Closed Access | Times Cited: 72
Semi-supervised learning for industrial fault detection and diagnosis: A systemic review
José Miguel Ramírez‐Sanz, Jose-Alberto Maestro-Prieto, Álvar Arnaiz‐González, et al.
ISA Transactions (2023) Vol. 143, pp. 255-270
Open Access | Times Cited: 38
Research on Fault Diagnosis of Rolling Bearing Based on Lightweight Model With Multiscale Features
Zong Meng, Cheng Luo, Jimeng Li, et al.
IEEE Sensors Journal (2023) Vol. 23, Iss. 12, pp. 13236-13247
Closed Access | Times Cited: 21
Cross-domain few-shot fault diagnosis based on meta-learning and domain adversarial graph convolutional network
Junwei Hu, Weigang Li, Yong Zhang, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 136, pp. 108970-108970
Closed Access | Times Cited: 13
Multi-scale residual neural network with enhanced gated recurrent unit for fault diagnosis of rolling bearing
Weiqing Liao, Wenlong Fu, Ke Yang, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 5, pp. 056114-056114
Closed Access | Times Cited: 11
A review: the application of generative adversarial network for mechanical fault diagnosis
Weiqing Liao, Ke Yang, Wenlong Fu, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 6, pp. 062002-062002
Closed Access | Times Cited: 7
Unsupervised Continual Source-Free Network for Fault Diagnosis of Machines Under Multiple Diagnostic Domains
Jipu Li, Ke Yue, Ruyi Huang, et al.
IEEE Sensors Journal (2023) Vol. 23, Iss. 8, pp. 8292-8303
Closed Access | Times Cited: 15
Unsupervised GAN with Fine-Tuning: A Novel Framework for Induction Motor Fault Diagnosis in Scarcely Labeled Sample Scenarios
Xin Chen, Zaigang Chen, Shiqian Chen, et al.
IEEE Transactions on Instrumentation and Measurement (2024) Vol. 73, pp. 1-11
Closed Access | Times Cited: 5
Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults
P. Maestro, José Miguel Ramírez‐Sanz, Andrés Bustillo, et al.
Applied Intelligence (2024) Vol. 54, Iss. 6, pp. 4525-4544
Open Access | Times Cited: 4
Cross-domain fault diagnosis of induction motor based on an improved unsupervised generative adversarial network and fine-tuning under limited labeled data
Xin Chen, Zaigang Chen
Measurement (2025), pp. 116988-116988
Closed Access
Regression loss-assisted conditional style generative adversarial network for virtual sample generation with small data in soft sensing
Xueyu Zhang, Qun-Xiong Zhu, Wei Ke, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 147, pp. 110306-110306
Closed Access
Multilevel feature encoder for transfer learning-based fault detection on acoustic signal
Dezheng Wang, Congyan Chen
Information Fusion (2025), pp. 103128-103128
Closed Access
A Bearing Fault Diagnosis Method under Small Sample Conditions Based on the Fractional Order Siamese Deep Residual Shrinkage Network
Tao Li, Xiaoting Wu, Zhuhui Luo, et al.
Fractal and Fractional (2024) Vol. 8, Iss. 3, pp. 134-134
Open Access | Times Cited: 2
Relation Awareness Network for Few-Shot Fine-Grained Fault Diagnosis
Yan Xu, Xinyao Ma, Xuan Wang, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 13, pp. 20949-20958
Closed Access | Times Cited: 2
Fault diagnosis of gas turbine generator bearings using enhanced valuable sample strategy and convolutional neural network
Xiaozhuo Xu, Zhiyuan Li, Yunji Zhao, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 1, pp. 015021-015021
Closed Access | Times Cited: 6
Extraction and diagnosis of rolling bearing fault signals based on improved wavelet transform
Zhiqing Cheng
Journal of Measurements in Engineering (2023) Vol. 11, Iss. 4, pp. 420-436
Open Access | Times Cited: 6
Imbalanced Fault Diagnosis Using Conditional Wasserstein Generative Adversarial Networks With Switchable Normalization
Wenlong Fu, Yupeng Chen, Hongyan Li, et al.
IEEE Sensors Journal (2023) Vol. 23, Iss. 23, pp. 29119-29130
Closed Access | Times Cited: 6
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
A Coarse-to-Fine Bilevel Adversarial Domain Adaptation Method for Fault Diagnosis of Rolling Bearings
Zhaohua Liu, Liang Chen, Hua‐Liang Wei, et al.
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-14
Open Access | Times Cited: 5
Multiscale Sparsity Measure Fusion for Bearing Performance Degradation Assessment
Qian Wang, Qiang Huang, Xingxing Jiang, et al.
IEEE Sensors Journal (2022) Vol. 23, Iss. 1, pp. 577-587
Closed Access | Times Cited: 5
Analysis and Diagnosis of Rolling Bearing Faults from the Perspective of Frequency Domain
Bin Chen, Yunxin Li, Rongxin Lv, et al.
Journal of Physics Conference Series (2022) Vol. 2403, Iss. 1, pp. 012018-012018
Open Access | Times Cited: 4
An Improved WGAN-Based Fault Diagnosis of Rolling Bearings
Chengli Zhao, Lu Zhang, Maiying Zhong
(2022), pp. 322-327
Closed Access | Times Cited: 3
Pattern classification of bearing faults in PMSM based on time domain feature ensembles
G Geetha, P Geethanjali
Engineering Research Express (2024) Vol. 6, Iss. 3, pp. 035205-035205
Closed Access
A light-weight factorized convolutions based dual-input fuzzy-CNN for efficient motor bearing fault diagnosis
Muhammad Irfan, Nabeel Ahmed Khan, Zohaib Mushtaq, et al.
Nondestructive Testing And Evaluation (2024), pp. 1-37
Closed Access
A novel progressive domain separation network with multi-metric ensemble quantification for open set fault diagnosis of motor bearings
Chaoyang Weng, Baochun Lu, L B Chen, et al.
Advanced Engineering Informatics (2024) Vol. 64, pp. 103060-103060
Closed Access
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