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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:

Deep residual learning with demodulated time-frequency features for fault diagnosis of planetary gearbox under nonstationary running conditions
Sai Ma, Fulei Chu, Qinkai Han
Mechanical Systems and Signal Processing (2019) Vol. 127, pp. 190-201
Closed Access | Times Cited: 102

Showing 1-25 of 102 citing articles:

Applications of machine learning to machine fault diagnosis: A review and roadmap
Yaguo Lei, Bin Yang, Xinwei Jiang, et al.
Mechanical Systems and Signal Processing (2020) Vol. 138, pp. 106587-106587
Open Access | Times Cited: 1948
Deep Residual Shrinkage Networks for Fault Diagnosis
Minghang Zhao, Shisheng Zhong, Xuyun Fu, et al.
IEEE Transactions on Industrial Informatics (2019) Vol. 16, Iss. 7, pp. 4681-4690
Closed Access | Times Cited: 928
A comprehensive review on convolutional neural network in machine fault diagnosis
Jinyang Jiao, Ming Zhao, Jing Lin, et al.
Neurocomputing (2020) Vol. 417, pp. 36-63
Open Access | Times Cited: 403
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights
Arman Malekloo, Ekin Özer, Mohammad AlHamaydeh, et al.
Structural Health Monitoring (2021) Vol. 21, Iss. 4, pp. 1906-1955
Open Access | Times Cited: 320
A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Zhiqin Zhu, Yangbo Lei, Guanqiu Qi, et al.
Measurement (2022) Vol. 206, pp. 112346-112346
Closed Access | Times Cited: 283
Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery
Shengnan Tang, Shouqi Yuan, Yong Zhu
IEEE Access (2019) Vol. 8, pp. 9335-9346
Open Access | Times Cited: 224
A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis
Wentao Mao, Wushi Feng, Yamin Liu, et al.
Mechanical Systems and Signal Processing (2020) Vol. 150, pp. 107233-107233
Closed Access | Times Cited: 190
Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis
Minghang Zhao, Shisheng Zhong, Xuyun Fu, et al.
IEEE Transactions on Industrial Electronics (2020) Vol. 68, Iss. 3, pp. 2587-2597
Closed Access | Times Cited: 165
Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising
Huan Wang, Zhiliang Liu, Dandan Peng, et al.
ISA Transactions (2021) Vol. 128, pp. 470-484
Closed Access | Times Cited: 147
A lightweight neural network with strong robustness for bearing fault diagnosis
Dechen Yao, Hengchang Liu, Jianwei Yang, et al.
Measurement (2020) Vol. 159, pp. 107756-107756
Closed Access | Times Cited: 116
Deep regularized variational autoencoder for intelligent fault diagnosis of rotor–bearing system within entire life-cycle process
Xiaoan Yan, Daoming She, Yadong Xu, et al.
Knowledge-Based Systems (2021) Vol. 226, pp. 107142-107142
Closed Access | Times Cited: 85
Deep morphological convolutional network for feature learning of vibration signals and its applications to gearbox fault diagnosis
Zhuang Ye, Jianbo Yu
Mechanical Systems and Signal Processing (2021) Vol. 161, pp. 107984-107984
Closed Access | Times Cited: 84
Transfer Relation Network for Fault Diagnosis of Rotating Machinery With Small Data
Na Lü, Huiyang Hu, Tao Yin, et al.
IEEE Transactions on Cybernetics (2021) Vol. 52, Iss. 11, pp. 11927-11941
Closed Access | Times Cited: 77
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
A novel RSG-based intelligent bearing fault diagnosis method for motors in high-noise industrial environment
Pin Lyu, Kewei Zhang, Wenbing Yu, et al.
Advanced Engineering Informatics (2022) Vol. 52, pp. 101564-101564
Closed Access | Times Cited: 60
Fault diagnosis of mechanical equipment in high energy consumption industries in China: A review
Yongjian Sun, Jian Wang, Xiaohong Wang
Mechanical Systems and Signal Processing (2022) Vol. 186, pp. 109833-109833
Closed Access | Times Cited: 43
Development of Intelligent Fault-Tolerant Control Systems with Machine Learning, Deep Learning, and Transfer Learning Algorithms: A Review
Arslan Ahmed Amin, Muhammad Sajid Iqbal, Muhammad Hamza Shahbaz
Expert Systems with Applications (2023) Vol. 238, pp. 121956-121956
Closed Access | Times Cited: 33
Domain fuzzy generalization networks for semi-supervised intelligent fault diagnosis under unseen working conditions
He Ren, Jun Wang, Zhongkui Zhu, et al.
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110579-110579
Closed Access | Times Cited: 27
Fault diagnosis of various rotating equipment using machine learning approaches – A review
S Manikandan, K Duraivelu
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (2020) Vol. 235, Iss. 2, pp. 629-642
Closed Access | Times Cited: 64
A Domain Adaptive Deep Transfer Learning Method for Gas-Insulated Switchgear Partial Discharge Diagnosis
Yanxin Wang, Jing Yan, Zhou Yang, et al.
IEEE Transactions on Power Delivery (2021) Vol. 37, Iss. 4, pp. 2514-2523
Closed Access | Times Cited: 54
A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification
Jinling Zhao, Lei Hu, Yingying Dong, et al.
International Journal of Applied Earth Observation and Geoinformation (2021) Vol. 102, pp. 102459-102459
Open Access | Times Cited: 42
A fault diagnosis method of rolling bearing based on improved deep residual shrinkage networks
Jinyu Tong, Shiyu Tang, Yi Wu, et al.
Measurement (2022) Vol. 206, pp. 112282-112282
Open Access | Times Cited: 36
Multi-level features fusion network-based feature learning for machinery fault diagnosis
Zhuang Ye, Jianbo Yu
Applied Soft Computing (2022) Vol. 122, pp. 108900-108900
Closed Access | Times Cited: 35
Deep sparse representation network for feature learning of vibration signals and its application in gearbox fault diagnosis
Mengqi Miao, Yuanhang Sun, Jianbo Yu
Knowledge-Based Systems (2022) Vol. 240, pp. 108116-108116
Closed Access | Times Cited: 34
A novel deep learning approach for intelligent fault diagnosis applications based on time-frequency images
Özgür Gültekin, E. Miné Cinar, Kemal Özkan, et al.
Neural Computing and Applications (2022) Vol. 34, Iss. 6, pp. 4803-4812
Closed Access | Times Cited: 33
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