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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:

Meta-learning for few-shot bearing fault diagnosis under complex working conditions
Chuanjiang Li, Shaobo Li, Ansi Zhang, et al.
Neurocomputing (2021) Vol. 439, pp. 197-211
Closed Access | Times Cited: 193

Showing 1-25 of 193 citing articles:

Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects
Yong Feng, Jinglong Chen, Jingsong Xie, et al.
Knowledge-Based Systems (2021) Vol. 235, pp. 107646-107646
Closed Access | Times Cited: 164
A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions
Hao Su, Ling Xiang, Aijun Hu, et al.
Mechanical Systems and Signal Processing (2022) Vol. 169, pp. 108765-108765
Closed Access | Times Cited: 164
Adversarial Domain-Invariant Generalization: A Generic Domain-Regressive Framework for Bearing Fault Diagnosis Under Unseen Conditions
Liang Chen, Qi Li, Changqing Shen, et al.
IEEE Transactions on Industrial Informatics (2021) Vol. 18, Iss. 3, pp. 1790-1800
Open Access | Times Cited: 137
Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives
Tongyang Pan, Jinglong Chen, Tianci Zhang, et al.
ISA Transactions (2021) Vol. 128, pp. 1-10
Closed Access | Times Cited: 133
Attention-based deep meta-transfer learning for few-shot fine-grained fault diagnosis
Chuanjiang Li, Shaobo Li, Huan Wang, et al.
Knowledge-Based Systems (2023) Vol. 264, pp. 110345-110345
Open Access | Times Cited: 107
A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains
Ke Zhao, Feng Jia, Haidong Shao
Knowledge-Based Systems (2022) Vol. 262, pp. 110203-110203
Closed Access | Times Cited: 99
Meta-learning with elastic prototypical network for fault transfer diagnosis of bearings under unstable speeds
Jingjie Luo, Haidong Shao, Jian Lin, et al.
Reliability Engineering & System Safety (2024) Vol. 245, pp. 110001-110001
Closed Access | Times Cited: 89
Meta-learning approaches for learning-to-learn in deep learning: A survey
Yingjie Tian, Xiaoxi Zhao, Wei Huang
Neurocomputing (2022) Vol. 494, pp. 203-223
Closed Access | Times Cited: 79
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
Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review
Shaohua Qiu, Xiaopeng Cui, Zuowei Ping, et al.
Sensors (2023) Vol. 23, Iss. 3, pp. 1305-1305
Open Access | Times Cited: 63
Counterfactual-augmented few-shot contrastive learning for machinery intelligent fault diagnosis with limited samples
Yunpeng Liu, Hongkai Jiang, Renhe Yao, et al.
Mechanical Systems and Signal Processing (2024) Vol. 216, pp. 111507-111507
Closed Access | Times Cited: 32
Small data challenges for intelligent prognostics and health management: a review
Chuanjiang Li, Shaobo Li, Yixiong Feng, et al.
Artificial Intelligence Review (2024) Vol. 57, Iss. 8
Open Access | Times Cited: 23
An information fusion-based meta transfer learning method for few-shot fault diagnosis under varying operating conditions
Cuiying Lin, Yun Kong, Qinkai Han, et al.
Mechanical Systems and Signal Processing (2024) Vol. 220, pp. 111652-111652
Closed Access | Times Cited: 17
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
A Novel Transformer-based Few-Shot Learning Method for Intelligent Fault Diagnosis with Noisy Labels under Varying Working Conditions
Haoyu Wang, Chuanjiang Li, Peng Ding, et al.
Reliability Engineering & System Safety (2024) Vol. 251, pp. 110400-110400
Closed Access | Times Cited: 14
Imbalanced Sample Selection With Deep Reinforcement Learning for Fault Diagnosis
Saite Fan, Xinmin Zhang, Zhihuan Song
IEEE Transactions on Industrial Informatics (2021) Vol. 18, Iss. 4, pp. 2518-2527
Closed Access | Times Cited: 69
Open-Set Fault Diagnosis via Supervised Contrastive Learning With Negative Out-of-Distribution Data Augmentation
Peng Peng, Jiaxun Lu, Tingyu Xie, et al.
IEEE Transactions on Industrial Informatics (2022) Vol. 19, Iss. 3, pp. 2463-2473
Closed Access | Times Cited: 42
A novel cross-domain fault diagnosis method based on model agnostic meta-learning
Tianyuan Yang, Tang Tang, Jingwei Wang, et al.
Measurement (2022) Vol. 199, pp. 111564-111564
Closed Access | Times Cited: 41
Meta-Transfer Metric Learning for Time Series Classification in 6G-Supported Intelligent Transportation Systems
Le Sun, Jiancong Liang, Chunjiong Zhang, et al.
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 25, Iss. 3, pp. 2757-2767
Closed Access | Times Cited: 37
Domain adaptation meta-learning network with discard-supplement module for few-shot cross-domain rotating machinery fault diagnosis
Yu Zhang, Dongying Han, Jinghui Tian, et al.
Knowledge-Based Systems (2023) Vol. 268, pp. 110484-110484
Closed Access | Times Cited: 28
Domain Discrepancy-Guided Contrastive Feature Learning for Few-Shot Industrial Fault Diagnosis Under Variable Working Conditions
Tianci Zhang, Jinglong Chen, Shen Liu, et al.
IEEE Transactions on Industrial Informatics (2023) Vol. 19, Iss. 10, pp. 10277-10287
Closed Access | Times Cited: 26
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
Multibranch and Multiscale Dynamic Convolutional Network for Small Sample Fault Diagnosis of Rotating Machinery
Haopeng Liang, Jie Cao, Xiaoqiang Zhao
IEEE Sensors Journal (2023) Vol. 23, Iss. 8, pp. 8973-8988
Closed Access | Times Cited: 22
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: 21
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