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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:

A Deep Learning Method for Bearing Fault Diagnosis Based on Time-Frequency Image
Jianyu Wang, Zhenling Mo, Heng Zhang, et al.
IEEE Access (2019) Vol. 7, pp. 42373-42383
Open Access | Times Cited: 179

Showing 1-25 of 179 citing articles:

A systematic review of deep transfer learning for machinery fault diagnosis
Chuan Li, Shaohui Zhang, Qin Yi, et al.
Neurocomputing (2020) Vol. 407, pp. 121-135
Closed Access | Times Cited: 348
Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study
Zhibin Zhao, Qiyang Zhang, Xiaolei Yu, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-28
Open Access | Times Cited: 329
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
Behnoush Rezaeianjouybari, Yi Shang
Measurement (2020) Vol. 163, pp. 107929-107929
Closed Access | Times Cited: 231
An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image
Ying Zhang, Kangshuo Xing, Ruxue Bai, et al.
Measurement (2020) Vol. 157, pp. 107667-107667
Closed Access | Times Cited: 223
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: 195
Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016
Xiaohan Chen, Rui Yang, Yihao Xue, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-21
Open Access | Times Cited: 190
A Survey of Predictive Maintenance: Systems, Purposes and Approaches
Yongyi Ran, Xin Zhou, Pengfeng Lin, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 176
Intelligent Mechanical Fault Diagnosis Using Multisensor Fusion and Convolution Neural Network
Tingli Xie, Xufeng Huang, Seung-Kyum Choi
IEEE Transactions on Industrial Informatics (2021) Vol. 18, Iss. 5, pp. 3213-3223
Closed Access | Times Cited: 163
Deep multi-scale convolutional transfer learning network: A novel method for intelligent fault diagnosis of rolling bearings under variable working conditions and domains
Bo Zhao, Xianmin Zhang, Zhenhui Zhan, et al.
Neurocomputing (2020) Vol. 407, pp. 24-38
Closed Access | Times Cited: 139
Data-driven fault diagnosis method based on the conversion of erosion operation signals into images and convolutional neural network
Zhijian Wang, Wenlei Zhao, Wenhua Du, et al.
Process Safety and Environmental Protection (2021) Vol. 149, pp. 591-601
Closed Access | Times Cited: 115
Multi-scale convolutional network with channel attention mechanism for rolling bearing fault diagnosis
Yajing Huang, Aihua Liao, Dingyu Hu, et al.
Measurement (2022) Vol. 203, pp. 111935-111935
Closed Access | Times Cited: 90
A Review of Data-Driven Machinery Fault Diagnosis Using Machine Learning Algorithms
Jian Cen, Zhuohong Yang, Xi Liu, et al.
Journal of Vibration Engineering & Technologies (2022) Vol. 10, Iss. 7, pp. 2481-2507
Closed Access | Times Cited: 88
Fine-tuning transfer learning based on DCGAN integrated with self-attention and spectral normalization for bearing fault diagnosis
Hongyu Zhong, Samson S. Yu, Hieu Trinh, et al.
Measurement (2023) Vol. 210, pp. 112421-112421
Closed Access | Times Cited: 70
Autocorrelation Aided Random Forest Classifier-Based Bearing Fault Detection Framework
Sayanjit Singha Roy, Sayantan Dey, Soumya Chatterjee
IEEE Sensors Journal (2020) Vol. 20, Iss. 18, pp. 10792-10800
Closed Access | Times Cited: 128
A Review on Prognostics Methods for Engineering Systems
Jian Guo, Zhaojun Li, Meiyan Li
IEEE Transactions on Reliability (2019) Vol. 69, Iss. 3, pp. 1110-1129
Closed Access | Times Cited: 125
Interpreting network knowledge with attention mechanism for bearing fault diagnosis
Zhibo Yang, Junpeng Zhang, Zhibin Zhao, et al.
Applied Soft Computing (2020) Vol. 97, pp. 106829-106829
Closed Access | Times Cited: 122
Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review
Zhibin Zhao, Jingyao Wu, Tianfu Li, et al.
Chinese Journal of Mechanical Engineering (2021) Vol. 34, Iss. 1
Open Access | Times Cited: 98
An Efficient Rolling Bearing Fault Diagnosis Method Based on Spark and Improved Random Forest Algorithm
Lanjun Wan, Kun Gong, Gen Zhang, et al.
IEEE Access (2021) Vol. 9, pp. 37866-37882
Open Access | Times Cited: 81
FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification
Rishikesh Magar, Lalit Ghule, Junhan Li, et al.
IEEE Access (2021) Vol. 9, pp. 25189-25199
Open Access | Times Cited: 72
A CNN-based transfer learning method for leakage detection of pipeline under multiple working conditions with AE signals
Pengqian Liu, Changhang Xu, Jing Xie, et al.
Process Safety and Environmental Protection (2022) Vol. 170, pp. 1161-1172
Closed Access | Times Cited: 48
A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors
Guoguo Wu, T. Y. Yan, Guolai Yang, et al.
Sensors (2022) Vol. 22, Iss. 21, pp. 8330-8330
Open Access | Times Cited: 46
A novel intelligent diagnosis method of rolling bearing and rotor composite faults based on vibration signal-to-image mapping and CNN-SVM
Hongwei Fan, Ceyi Xue, Jiateng Ma, et al.
Measurement Science and Technology (2022) Vol. 34, Iss. 4, pp. 044008-044008
Closed Access | Times Cited: 43
A New Adversarial Domain Generalization Network Based on Class Boundary Feature Detection for Bearing Fault Diagnosis
Jingde Li, Changqing Shen, Lin Kong, et al.
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-9
Open Access | Times Cited: 42
Continual learning for predictive maintenance: Overview and challenges
Julio Hurtado, Dario Salvati, Rudy Semola, et al.
Intelligent Systems with Applications (2023) Vol. 19, pp. 200251-200251
Open Access | Times Cited: 32
A Novel Wind Turbine Rolling Element Bearing Fault Diagnosis Method Based on CEEMDAN and Improved TFR Demodulation Analysis
Dahai Zhang, Yiming Wang, Yongjian Jiang, et al.
Energies (2024) Vol. 17, Iss. 4, pp. 819-819
Open Access | Times Cited: 11
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