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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 deep learning based rotating machinery health prognostics toward few-shot prognostics
Peng Ding, Minping Jia, Xiaoli Zhao
Applied Soft Computing (2021) Vol. 104, pp. 107211-107211
Closed Access | Times Cited: 63

Showing 1-25 of 63 citing articles:

Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods
Carlos Ferreira, Gil Gonçalves
Journal of Manufacturing Systems (2022) Vol. 63, pp. 550-562
Open Access | Times Cited: 166
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
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
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: 108
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
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
Cross-domain fault diagnosis of bearing using improved semi-supervised meta-learning towards interference of out-of-distribution samples
Jian Lin, Haidong Shao, Zhishan Min, et al.
Knowledge-Based Systems (2022) Vol. 252, pp. 109493-109493
Closed Access | Times Cited: 52
RUL prediction for rolling bearings based on Convolutional Autoencoder and status degradation model
Weiyang Xu, Quan Jiang, Yehu Shen, et al.
Applied Soft Computing (2022) Vol. 130, pp. 109686-109686
Closed Access | Times Cited: 38
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
A contrastive learning framework enhanced by unlabeled samples for remaining useful life prediction
Ziqian Kong, Xiaohang Jin, Zhengguo Xu, et al.
Reliability Engineering & System Safety (2023) Vol. 234, pp. 109163-109163
Closed Access | Times Cited: 22
Few-shot fault diagnosis of rolling bearing under variable working conditions based on ensemble meta-learning
Changchang Che, Huawei Wang, Minglan Xiong, et al.
Digital Signal Processing (2022) Vol. 131, pp. 103777-103777
Closed Access | Times Cited: 33
Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review
Somayeh Bakhtiari Ramezani, Logan Cummins, Brad Killen, et al.
IEEE Access (2023) Vol. 11, pp. 41741-41769
Open Access | Times Cited: 17
Gradient flow-based meta generative adversarial network for data augmentation in fault diagnosis
Rugen Wang, Zhuyun Chen, Weihua Li
Applied Soft Computing (2023) Vol. 142, pp. 110313-110313
Closed Access | Times Cited: 17
Federated temporal-context contrastive learning for fault diagnosis using multiple datasets with insufficient labels
Haowen Zheng, Hui Liu, Zhenyu Liu, et al.
Advanced Engineering Informatics (2024) Vol. 60, pp. 102432-102432
Closed Access | Times Cited: 5
A transferable neural network method for remaining useful life prediction
Rui He, Zhigang Tian, Ming J. Zuo
Mechanical Systems and Signal Processing (2022) Vol. 183, pp. 109608-109608
Closed Access | Times Cited: 27
Revolution and peak discrepancy-based domain alignment method for bearing fault diagnosis under very low-speed conditions
Seung-Yun Lee, Sungjong Kim, Su J. Kim, et al.
Expert Systems with Applications (2024) Vol. 251, pp. 124084-124084
Closed Access | Times Cited: 5
An autoregressive model-based degradation trend prognosis considering health indicators with multiscale attention information
Jichao Zhuang, Yudong Cao, Yifei Ding, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 131, pp. 107868-107868
Closed Access | Times Cited: 4
Health prognosis of bearings based on transferable autoregressive recurrent adaptation with few-shot learning
Jichao Zhuang, Minping Jia, Cheng‐Geng Huang, et al.
Mechanical Systems and Signal Processing (2024) Vol. 211, pp. 111186-111186
Closed Access | Times Cited: 4
Multi-stream domain adversarial prototype network for integrated smart roller TBM main bearing fault diagnosis across various low rotating speeds
Xingchen Fu, Keming Jiao, Jianfeng Tao, et al.
Reliability Engineering & System Safety (2024) Vol. 250, pp. 110284-110284
Closed Access | Times Cited: 4
A new cross-domain bearing fault diagnosis method with few samples under different working conditions
Xingjun Dong, Changsheng Zhang, Hanrui Liu, et al.
Journal of Manufacturing Processes (2025) Vol. 135, pp. 359-374
Closed Access
A survey on few-shot learning for remaining useful life prediction
Renpeng Mo, Han Zhou, Hongpeng Yin, et al.
Reliability Engineering & System Safety (2025), pp. 110850-110850
Closed Access
A Meta-Transfer-Driven Method for Predicting the Remaining Useful Life of Rolling Bearing with Few Shot Data
Daoming She, Yangyang Luo, Yitian Wang, et al.
(2025)
Closed Access
A meta-transfer learning prediction method with few-shot data for the remaining useful life of rolling bearing
Daoming She, Yudan Duan, Zhichao Yang, et al.
Structural Health Monitoring (2025)
Closed Access
Meta-learning for enhancing machines' intelligence in manufacturing
Goran D. Putnik
Tehnika (2025) Vol. 80, Iss. 1, pp. 57-75
Open Access
WEFormer: Classification for physiological time series with small sample sizes based on wavelet decomposition and time series foundation models
Xiangdong He, Zhou Jiang, Xiaodong Wu, et al.
Biomedical Signal Processing and Control (2025) Vol. 107, pp. 107842-107842
Closed Access
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