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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:

Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method
Jun Li, Yongbao Liu, Qijie Li
Measurement (2021) Vol. 189, pp. 110500-110500
Closed Access | Times Cited: 143

Showing 1-25 of 143 citing articles:

Attention mechanism in intelligent fault diagnosis of machinery: A review of technique and application
Haixin Lv, Jinglong Chen, Tongyang Pan, et al.
Measurement (2022) Vol. 199, pp. 111594-111594
Closed Access | Times Cited: 148
Few-Shot Cross-Domain Fault Diagnosis of Bearing Driven by Task-Supervised ANIL
Haidong Shao, Xiangdong Zhou, Jian Lin, et al.
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 13, pp. 22892-22902
Closed Access | Times Cited: 70
Feature extraction of multi-sensors for early bearing fault diagnosis using deep learning based on minimum unscented kalman filter
Haihong Tang, Yanmin Tang, Yuxiang Su, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 127, pp. 107138-107138
Closed Access | Times Cited: 53
A Scalo Gram-Based CNN Ensemble Method With Density-Aware SMOTE Oversampling for Improving Bearing Fault Diagnosis
Muhammad Irfan, Zohaib Mushtaq, Nabeel Ahmed Khan, et al.
IEEE Access (2023) Vol. 11, pp. 127783-127799
Open Access | Times Cited: 51
Improving Bearing Fault Identification by Using Novel Hybrid Involution-Convolution Feature Extraction With Adversarial Noise Injection in Conditional GANs
Muhammad Irfan, Zohaib Mushtaq, Nabeel Ahmed Khan, et al.
IEEE Access (2023) Vol. 11, pp. 118253-118267
Open Access | Times Cited: 50
Enhanced transfer learning method for rolling bearing fault diagnosis based on linear superposition network
Chunran Huo, Quan Jiang, Yehu Shen, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 121, pp. 105970-105970
Closed Access | Times Cited: 49
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
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
A systematic literature review of deep learning for vibration-based fault diagnosis of critical rotating machinery: Limitations and challenges
Omri Matania, Itai Dattner, Jacob Bortman, et al.
Journal of Sound and Vibration (2024) Vol. 590, pp. 118562-118562
Closed Access | Times Cited: 15
Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari, et al.
Information Fusion (2022) Vol. 93, pp. 85-117
Closed Access | Times Cited: 65
Intelligent fault diagnosis of rolling bearings under varying operating conditions based on domain-adversarial neural network and attention mechanism
Hao Wu, Jimeng Li, Qingyu Zhang, et al.
ISA Transactions (2022) Vol. 130, pp. 477-489
Closed Access | Times Cited: 62
Data-augmented wavelet capsule generative adversarial network for rolling bearing fault diagnosis
Yunpeng Liu, Hongkai Jiang, Chaoqiang Liu, et al.
Knowledge-Based Systems (2022) Vol. 252, pp. 109439-109439
Closed Access | Times Cited: 59
A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors
Guoguo Wu, T. Y. Yan, Guolai Yang, et al.
Sensors (2022) Vol. 22, Iss. 21, pp. 8330-8330
Open Access | Times Cited: 46
A class-aware supervised contrastive learning framework for imbalanced fault diagnosis
Jiyang Zhang, Jianxiao Zou, Zhiheng Su, et al.
Knowledge-Based Systems (2022) Vol. 252, pp. 109437-109437
Closed Access | Times Cited: 42
Multi-perspective deep transfer learning model: A promising tool for bearing intelligent fault diagnosis under varying working conditions
Xue‐Gang Li, Xingxing Jiang, Qian Wang, et al.
Knowledge-Based Systems (2022) Vol. 243, pp. 108443-108443
Closed Access | Times Cited: 38
WSAFormer-DFFN: A model for rotating machinery fault diagnosis using 1D window-based multi-head self-attention and deep feature fusion network
Qingzhe Wei, Xincheng Tian, Long Cui, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 124, pp. 106633-106633
Closed Access | Times Cited: 30
Adaptive Class Center Generalization Network: A Sparse Domain-Regressive Framework for Bearing Fault Diagnosis Under Unknown Working Conditions
Wang Bin, Long Wen, Xinyu Li, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-11
Closed Access | Times Cited: 29
Intelligent Fault Diagnosis of Rolling Bearings Using Efficient and Lightweight ResNet Networks Based on an Attention Mechanism (September 2022)
Meng Chang, Dechen Yao, Jianwei Yang
IEEE Sensors Journal (2023) Vol. 23, Iss. 9, pp. 9136-9145
Closed Access | Times Cited: 27
A class-level matching unsupervised transfer learning network for rolling bearing fault diagnosis under various working conditions
Chunran Huo, Quan Jiang, Yehu Shen, et al.
Applied Soft Computing (2023) Vol. 146, pp. 110739-110739
Closed Access | Times Cited: 24
Lightweight Convolutional Transformers Enhanced Meta-Learning for Compound Fault Diagnosis of Industrial Robot
Chong Chen, Tao Wang, Chao Liu, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-12
Open Access | Times Cited: 22
A generalized method for diagnosing multi-faults in rotating machines using imbalance datasets of different sensor modalities
Rismaya Kumar Mishra, Anurag Choudhary, Shahab Fatima, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 132, pp. 107973-107973
Closed Access | Times Cited: 11
Electro-hydraulic SBW fault diagnosis method based on novel 1DCNN-LSTM with attention mechanisms and transfer learning
Senhao Zhang, Weihe Liang, Wanzhong Zhao, et al.
Mechanical Systems and Signal Processing (2024) Vol. 220, pp. 111644-111644
Closed Access | Times Cited: 10
Few-Shot Class-Incremental Learning with Adjustable Pseudo-Incremental Sessions for Bearing Fault Diagnosis
Hongyan Zhu, Changqing Shen, Jiaan Wang, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 12, pp. 19543-19552
Closed Access | Times Cited: 8
Gearbox fault diagnosis method based on lightweight channel attention mechanism and transfer learning
Xuemin Cheng, Shuihai Dou, Yanping Du, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Research on bearing fault diagnosis based on novel MRSVD-CWT and improved CNN-LSTM
Yuan Guo, Jun Zhou, Zhenbiao Dong, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 9, pp. 095003-095003
Closed Access | Times Cited: 7
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