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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:

Progressively Balanced Supervised Contrastive Representation Learning for Long-Tailed Fault Diagnosis
Peng Peng, Jiaxun Lu, Shuting Tao, et al.
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-12
Closed Access | Times Cited: 21

Showing 21 citing articles:

Extended attention signal transformer with adaptive class imbalance loss for Long-tailed intelligent fault diagnosis of rotating machinery
Shuyuan Chang, Liyong Wang, Mingkuan Shi, et al.
Advanced Engineering Informatics (2024) Vol. 60, pp. 102436-102436
Closed Access | Times Cited: 18
Attention-Embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis
Jing-Xiao Liao, Hangcheng Dong, Zhi-Qi Sun, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-13
Open Access | Times Cited: 32
A unified out-of-distribution detection framework for trustworthy prognostics and health management in renewable energy systems
Wenzhen Xie, Te Han, Zhongyi Pei, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 125, pp. 106707-106707
Closed Access | Times Cited: 23
A holistic semi-supervised method for imbalanced fault diagnosis of rotational machinery with out-of-distribution samples
Zhangjun Wu, Renli Xu, Yuansheng Luo, et al.
Reliability Engineering & System Safety (2024) Vol. 250, pp. 110297-110297
Closed Access | Times Cited: 9
Imbalance fault diagnosis under long-tailed distribution: Challenges, solutions and prospects
Zhuohang Chen, Jinglong Chen, Yong Feng, et al.
Knowledge-Based Systems (2022) Vol. 258, pp. 110008-110008
Closed Access | Times Cited: 35
A Transformer-based self-supervised learning model for fault diagnosis of air-conditioning systems with limited labeled data
Hua Mei, Ke Yan, Xin Li
Engineering Applications of Artificial Intelligence (2025) Vol. 146, pp. 110331-110331
Closed Access
Self-supervised feature extraction via time–frequency contrast for intelligent fault diagnosis of rotating machinery
Yang Liu, Weigang Wen, Yihao Bai, et al.
Measurement (2023) Vol. 210, pp. 112551-112551
Closed Access | Times Cited: 14
An adaptive fault diagnosis framework under class-imbalanced conditions based on contrastive augmented deep reinforcement learning
Qin Zhao, Yu Ding, Chen Lü, et al.
Expert Systems with Applications (2023) Vol. 234, pp. 121001-121001
Closed Access | Times Cited: 14
Enhanced predictive modeling of rotating machinery remaining useful life by using separable convolution backbone networks
Li Zou, Cong Ma, Jun Hu, et al.
Applied Soft Computing (2024) Vol. 156, pp. 111493-111493
Closed Access | Times Cited: 4
A supervised contrastive learning method based on online complement strategy for long-tailed fine-grained fault diagnosis
Zhiqian Zhao, Yinghou Jiao, Yeyin Xu, et al.
Advanced Engineering Informatics (2025) Vol. 64, pp. 103079-103079
Closed Access
Frequency-assisted Contrastive Learning with Cyclic Fine-tuning for Rotating Machinery Fault Diagnosis under Limited Labeled Data
Xinyu Li, Changming Cheng, Zhike Peng
IEEE Transactions on Instrumentation and Measurement (2025) Vol. 74, pp. 1-11
Closed Access
Reserving embedding space for new fault types: A new continual learning method for bearing fault diagnosis
Hongyan Zhu, Changqing Shen, Lin Li, et al.
Reliability Engineering & System Safety (2024) Vol. 252, pp. 110433-110433
Closed Access | Times Cited: 3
SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-Hoc Interpretable Fault Diagnosis With Limited Fault Samples
Mengxuan Li, Peng Peng, Jingxin Zhang, et al.
IEEE Transactions on Neural Networks and Learning Systems (2023) Vol. 35, Iss. 5, pp. 6194-6205
Open Access | Times Cited: 8
Granularity knowledge-sharing supervised contrastive learning framework for long-tailed fault diagnosis of rotating machinery
Shuyuan Chang, Liyong Wang, Mingkuan Shi, et al.
Knowledge-Based Systems (2024) Vol. 301, pp. 112354-112354
Closed Access | Times Cited: 2
A dual-view network for fault diagnosis in rotating machinery using unbalanced data
Zixu Chen, Wennian Yu, Chengcheng Kong, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 11, pp. 115107-115107
Closed Access | Times Cited: 5
Debiased Contrastive Learning for Time-Series Representation Learning and Fault Detection
Kexin Zhang, Rongyao Cai, Chunlin Zhou, et al.
IEEE Transactions on Industrial Informatics (2024) Vol. 20, Iss. 5, pp. 7641-7653
Closed Access | Times Cited: 1
Active diversification of head-class features in bilateral-expert models for enhanced tail-class optimization in long-tailed classification
Jianting Chen, Ling Ding, Yunxiao Yang, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 106982-106982
Closed Access | Times Cited: 2
Imbalanced Fault Diagnosis by Supervised Contrastive Learning
Peng Peng, Xinyue Wang, Jiaxun Lu, et al.
2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (2022), pp. 689-692
Closed Access | Times Cited: 3
Hard sample mining-enabled supervised contrastive feature learning for wind turbine pitch system fault diagnosis
Zixuan Wang, Ke Ma, Bo Qin, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 11, pp. 116203-116203
Open Access
SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples
Mengxuan Li, Peng Peng, Jingxin Zhang, et al.
arXiv (Cornell University) (2023)
Open Access
NeuTR: A Learnable Neural Data Augmentation Strategy for Supervised Contrastive Representation Learning in Fault Diagnosis with Limited Fault Data
Luyao Zhang, Peng Peng, Heming Zhang
2021 China Automation Congress (CAC) (2023), pp. 1483-1489
Closed Access
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