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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:

Attention-based multiscale denoising residual convolutional neural networks for fault diagnosis of rotating machinery
Yadong Xu, Xiaoan Yan, Ke Feng, et al.
Reliability Engineering & System Safety (2022) Vol. 226, pp. 108714-108714
Closed Access | Times Cited: 63

Showing 26-50 of 63 citing articles:

DPCCNN: A new lightweight fault diagnosis model for small samples and high noise problem
Jiabin Zhang, Zhiqian Zhao, Yinghou Jiao, et al.
Neurocomputing (2025), pp. 129526-129526
Closed Access
MSIFT: A novel end-to-end mechanical fault diagnosis framework under limited & imbalanced data using multi-source information fusion
Yue Yu, Hamid Reza Karimi, Len Gelman, et al.
Expert Systems with Applications (2025), pp. 126947-126947
Open Access
Image texture feature fusion enhancement for bearing fault diagnosis based on maximum gradient
Yongjian Sun, Gang Yu, Wei Wang
Reliability Engineering & System Safety (2025), pp. 111009-111009
Closed Access
A progressive multi-source domain adaptation method for bearing fault diagnosis
Xiaorong Zheng, Zhiwei He, Jiahao Nie, et al.
Applied Acoustics (2023) Vol. 216, pp. 109797-109797
Closed Access | Times Cited: 11
A novel rolling bearing fault diagnosis method based on time-series fusion transformer with interpretability analysis
You Keshun, Lian Zengwei, Ronghua Chen, et al.
Nondestructive Testing And Evaluation (2024), pp. 1-27
Closed Access | Times Cited: 3
Self-driven continual learning for class-added motor fault diagnosis based on unseen fault detector and propensity distillation
Ao Ding, Xiaojian Yi, Yong Qin, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 127, pp. 107382-107382
Closed Access | Times Cited: 10
Novel imbalanced subdomain adaption multiscale convolutional network for cross-domain unsupervised fault diagnosis of rolling bearings
Tianlong Huo, Linfeng Deng, Bo Zhang, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 1, pp. 015905-015905
Closed Access | Times Cited: 9
Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders
Cihan Ateş, Tobias Höfchen, Mario Witt, et al.
Sensors (2023) Vol. 23, Iss. 22, pp. 9212-9212
Open Access | Times Cited: 9
Fault diagnosis of a marine power-generation diesel engine based on the Gramian angular field and a convolutional neural network
Congyue Li, Yihuai Hu, Jiawei Jiang, et al.
Journal of Zhejiang University. Science A (2024) Vol. 25, Iss. 6, pp. 470-482
Closed Access | Times Cited: 3
Robust Multiple-Fault Diagnosis of PMSM Drives Under Variant Operations and Noisy Conditions
Mahmoud S. Mahmoud, Huynh Van Khang, Jagath Sri Lal Senanyaka, et al.
IEEE Open Journal of the Industrial Electronics Society (2023) Vol. 4, pp. 762-772
Open Access | Times Cited: 8
Rolling bearing fault diagnosis under time-varying speeds based on time-characteristic order spectrum and multi-scale domain adaptation network
Zhenli Xu, Guiji Tang, Bin Pang, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 12, pp. 125118-125118
Closed Access | Times Cited: 7
Machinery degradation trend prediction considering temporal distribution discrepancy between degradation stages
Shudong Ou, Ming Zhao, Hao Wu, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 131, pp. 107872-107872
Closed Access | Times Cited: 2
Contrast Learning with Hard Example Mining for Few-shot Fault Diagnosis of Rolling Bearings
Zenghui An, Houliang Wang, Yinglong Yan, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 10, pp. 106121-106121
Closed Access | Times Cited: 2
Joint Threshold Learning Convolutional Networks for Intelligent Fault Diagnosis Under Nonstationary Conditions
Sheng Li, Yadong Xu, Ke Feng, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-11
Closed Access | Times Cited: 5
A GTI&Ada-Act LMCNN Method for Intelligent Fault Diagnosis of Motor Rotor-Bearing Unit Under Variable Conditions
Hongwei Fan, Zhongfu Ren, Xiangang Cao, et al.
IEEE Transactions on Instrumentation and Measurement (2024) Vol. 73, pp. 1-14
Closed Access | Times Cited: 1
A Multi-scale Convolutional Autoencoder with Attention Mechanism for Fault Diagnosis of Rotating Machinery
Zihao Lei, Hongguang Yun, Feiyu Tian, et al.
Springer eBooks (2024), pp. 601-617
Closed Access | Times Cited: 1
A lightweight improved residual neural network for bearing fault diagnosis
Huaqing Wang, Zhenbao Fu, Tianjiao Lin, et al.
Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science (2024)
Closed Access | Times Cited: 1
Prediction of mechanical properties of rolled steel based on dual-attention multiscale convolutional neural network
Qiwen Zhang, Wenkui Wu, Xingchang Tang, et al.
Materials Today Communications (2024) Vol. 41, pp. 110432-110432
Closed Access | Times Cited: 1
A case-learning-based paradigm for quantitative recommendation of fault diagnosis algorithms: A case study of gearbox
Xinyu Zou, Laifa Tao, Lulu Sun, et al.
Reliability Engineering & System Safety (2023) Vol. 237, pp. 109372-109372
Closed Access | Times Cited: 4
Multi-sensor and multi-level information fusion model for compressor blade crack detection
Tianchi Ma, Junxian Shen, Di Song, et al.
Measurement (2023) Vol. 222, pp. 113622-113622
Closed Access | Times Cited: 2
A multi-scale collaborative fusion residual neural network-based approach for bearing fault diagnosis
Chen Qian, Jun Gao, Xing Shao, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 4, pp. 046204-046204
Open Access
A novel residual global context shrinkage network based fault diagnosis method for rotating machinery under noisy conditions
Jinyu Tong, Shiyu Tang, Jinde Zheng, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 7, pp. 075108-075108
Closed Access
Fault diagnosis based on feature enhancement multiscale network under nonstationary conditions
Yao Liu, Haoyuan Dong, Wei Ma
Aerospace Systems (2024)
Closed Access
IoT-Based Adaptive Multiplication-Convolution Sparse Denoising for Equipment Edge Condition Evaluation
Qihang Wu, Xiaoxi Ding, Wenhao Cheng, et al.
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 18, pp. 29661-29672
Closed Access
DMWMN: A Deep Modulation Network for Gearbox Intelligent Fault Detection Under Variable Working Conditions
Junchao Guo, Qingbo He, Fengshou Gu, et al.
IEEE Transactions on Systems Man and Cybernetics Systems (2024) Vol. 54, Iss. 10, pp. 6082-6092
Closed Access

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