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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:

Dual-Threshold Attention-Guided GAN and Limited Infrared Thermal Images for Rotating Machinery Fault Diagnosis Under Speed Fluctuation
Haidong Shao, Wei Li, Baoping Cai, et al.
IEEE Transactions on Industrial Informatics (2023) Vol. 19, Iss. 9, pp. 9933-9942
Closed Access | Times Cited: 177

Showing 1-25 of 177 citing articles:

Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network
Xingkai Chen, Haidong Shao, Yiming Xiao, et al.
Mechanical Systems and Signal Processing (2023) Vol. 198, pp. 110427-110427
Open Access | Times Cited: 149
Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals
Jian Lin, Haidong Shao, Xiangdong Zhou, et al.
Expert Systems with Applications (2023) Vol. 230, pp. 120696-120696
Open Access | Times Cited: 118
Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy
Ke Zhao, Junchen Hu, Haidong Shao, et al.
Reliability Engineering & System Safety (2023) Vol. 236, pp. 109246-109246
Closed Access | Times Cited: 93
A novel digital twin-driven approach based on physical-virtual data fusion for gearbox fault diagnosis
Jingyan Xia, Ruyi Huang, Zhuyun Chen, et al.
Reliability Engineering & System Safety (2023) Vol. 240, pp. 109542-109542
Closed Access | Times Cited: 50
An Adaptive Domain Adaptation Method for Rolling Bearings’ Fault Diagnosis Fusing Deep Convolution and Self-Attention Networks
Xiao Yu, Youjie Wang, Zhongting Liang, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-14
Closed Access | Times Cited: 45
Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning
You Keshun, Guangqi Qiu, Yingkui Gu
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109793-109793
Closed Access | Times Cited: 44
Digital twin-assisted multiscale residual-self-attention feature fusion network for hypersonic flight vehicle fault diagnosis
Yutong Dong, Hongkai Jiang, Zhenghong Wu, et al.
Reliability Engineering & System Safety (2023) Vol. 235, pp. 109253-109253
Closed Access | Times Cited: 43
An efficient lightweight neural network using BiLSTM-SCN-CBAM with PCA-ICEEMDAN for diagnosing rolling bearing faults
You Keshun, Guangqi Qiu, Yingkui Gu
Measurement Science and Technology (2023) Vol. 34, Iss. 9, pp. 094001-094001
Closed Access | Times Cited: 41
Toward Efficient and Interpretative Rolling Bearing Fault Diagnosis via Quadratic Neural Network With Bi-LSTM
You Keshun, Wang Puzhou, Yingkui Gu
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 13, pp. 23002-23019
Closed Access | Times Cited: 32
Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data
Weihan Li, Dunke Liu, Yang Li, et al.
Structural Health Monitoring (2024)
Closed Access | Times Cited: 24
Interpretable modulated differentiable STFT and physics-informed balanced spectrum metric for freight train wheelset bearing cross-machine transfer fault diagnosis under speed fluctuations
Chao He, Hongmei Shi, Ruixin Li, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102568-102568
Open Access | Times Cited: 17
Attention on the key modes: Machinery fault diagnosis transformers through variational mode decomposition
Hebin Liu, Qizhi Xu, Xiaolin Han, et al.
Knowledge-Based Systems (2024) Vol. 289, pp. 111479-111479
Closed Access | Times Cited: 16
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
Frequency slice graph spectrum model and its application in bearing fault feature extraction
Kun Zhang, Yanlei Liu, Long Zhang, et al.
Mechanical Systems and Signal Processing (2025) Vol. 226, pp. 112383-112383
Closed Access | Times Cited: 4
A novel deep clustering network using multi-representation autoencoder and adversarial learning for large cross-domain fault diagnosis of rolling bearings
Haoran Wen, Wei Guo, Xiang Li
Expert Systems with Applications (2023) Vol. 225, pp. 120066-120066
Closed Access | Times Cited: 39
A transfer learning strategy based on numerical simulation driving 1D Cycle-GAN for bearing fault diagnosis
Xiaoyang Liu, Shulin Liu, Jiawei Xiang, et al.
Information Sciences (2023) Vol. 642, pp. 119175-119175
Closed Access | Times Cited: 39
Conditional distribution-guided adversarial transfer learning network with multi-source domains for rolling bearing fault diagnosis
Zhenghong Wu, Hongkai Jiang, Shaowei Liu, et al.
Advanced Engineering Informatics (2023) Vol. 56, pp. 101993-101993
Closed Access | Times Cited: 31
C-ECAFormer: A new lightweight fault diagnosis framework towards heavy noise and small samples
Jie Wang, Haidong Shao, Shen Yan, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 107031-107031
Open Access | Times Cited: 31
A 3-D Attention-Enhanced Hybrid Neural Network for Turbofan Engine Remaining Life Prediction Using CNN and BiLSTM Models
You Keshun, Guangqi Qiu, Yingkui Gu
IEEE Sensors Journal (2023) Vol. 24, Iss. 14, pp. 21893-21905
Closed Access | Times Cited: 29
Interactive channel attention for rotating component fault detection with strong noise and limited data
Jianguo Miao, Congying Deng, Heng Zhang, et al.
Applied Soft Computing (2023) Vol. 138, pp. 110171-110171
Closed Access | Times Cited: 25
TSN: A novel intelligent fault diagnosis method for bearing with small samples under variable working conditions
Peiming Shi, Shuping Wu, Xuefang Xu, et al.
Reliability Engineering & System Safety (2023) Vol. 240, pp. 109575-109575
Closed Access | Times Cited: 23
Physics-informed multi-state temporal frequency network for RUL prediction of rolling bearings
Shilong Yang, Baoping Tang, Weiying Wang, et al.
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109716-109716
Closed Access | Times Cited: 22
Multiple Source-Free Domain Adaptation Network Based on Knowledge Distillation for Machinery Fault Diagnosis
Ke Yue, Jipu Li, Zhuyun Chen, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-11
Closed Access | Times Cited: 21
A digital twin-driven approach for partial domain fault diagnosis of rotating machinery
Jingyan Xia, Zhuyun Chen, Jiaxian Chen, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 131, pp. 107848-107848
Closed Access | Times Cited: 11
MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios
Maoyou Ye, Xiaoan Yan, Xing Hua, et al.
Expert Systems with Applications (2024) Vol. 259, pp. 125214-125214
Closed Access | Times Cited: 11
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