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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 learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
Zhibin Zhao, Tianfu Li, Jingyao Wu, et al.
ISA Transactions (2020) Vol. 107, pp. 224-255
Open Access | Times Cited: 425

Showing 1-25 of 425 citing articles:

A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
Weihua Li, Ruyi Huang, Jipu Li, et al.
Mechanical Systems and Signal Processing (2021) Vol. 167, pp. 108487-108487
Open Access | Times Cited: 520
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: 325
WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis
Tianfu Li, Zhibin Zhao, Chuang Sun, et al.
IEEE Transactions on Systems Man and Cybernetics Systems (2021) Vol. 52, Iss. 4, pp. 2302-2312
Open Access | Times Cited: 302
The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study
Tianfu Li, Zheng Zhou, Sinan Li, et al.
Mechanical Systems and Signal Processing (2021) Vol. 168, pp. 108653-108653
Closed Access | Times Cited: 302
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: 281
Multireceptive Field Graph Convolutional Networks for Machine Fault Diagnosis
Tianfu Li, Zhibin Zhao, Chuang Sun, et al.
IEEE Transactions on Industrial Electronics (2020) Vol. 68, Iss. 12, pp. 12739-12749
Closed Access | Times Cited: 249
Few-shot transfer learning for intelligent fault diagnosis of machine
Jingyao Wu, Zhibin Zhao, Chuang Sun, et al.
Measurement (2020) Vol. 166, pp. 108202-108202
Closed Access | Times Cited: 221
Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions
Tianfu Li, Zhibin Zhao, Chuang Sun, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-10
Closed Access | Times Cited: 195
A Hybrid Generalization Network for Intelligent Fault Diagnosis of Rotating Machinery Under Unseen Working Conditions
Te Han, Yan‐Fu Li, Min Qian
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-11
Closed Access | Times Cited: 178
Modified Stacked Autoencoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery
Haidong Shao, Min Xia, Jiafu Wan, et al.
IEEE/ASME Transactions on Mechatronics (2021) Vol. 27, Iss. 1, pp. 24-33
Open Access | Times Cited: 167
Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings
Yifei Ding, Jichao Zhuang, Peng Ding, et al.
Reliability Engineering & System Safety (2021) Vol. 218, pp. 108126-108126
Closed Access | Times Cited: 148
Fault diagnosis for small samples based on attention mechanism
Xin Zhang, Chao He, Yanping Lu, et al.
Measurement (2021) Vol. 187, pp. 110242-110242
Closed Access | Times Cited: 139
Physics-Informed LSTM hyperparameters selection for gearbox fault detection
Yuejian Chen, Meng Rao, Ke Feng, et al.
Mechanical Systems and Signal Processing (2022) Vol. 171, pp. 108907-108907
Closed Access | Times Cited: 135
A Time Series Transformer based method for the rotating machinery fault diagnosis
Yuhong Jin, Lei Hou, Yushu Chen
Neurocomputing (2022) Vol. 494, pp. 379-395
Closed Access | Times Cited: 128
Deep-Learning-Based Open Set Fault Diagnosis by Extreme Value Theory
Xiaolei Yu, Zhibin Zhao, Xingwu Zhang, et al.
IEEE Transactions on Industrial Informatics (2021) Vol. 18, Iss. 1, pp. 185-196
Closed Access | Times Cited: 121
FGDAE: A new machinery anomaly detection method towards complex operating conditions
Shen Yan, Haidong Shao, Zhishan Min, et al.
Reliability Engineering & System Safety (2023) Vol. 236, pp. 109319-109319
Open Access | Times Cited: 112
Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in transformer
Yiming Xiao, Haidong Shao, Minjie Feng, et al.
Journal of Manufacturing Systems (2023) Vol. 70, pp. 186-201
Open Access | Times Cited: 107
Bayesian variational transformer: A generalizable model for rotating machinery fault diagnosis
Yiming Xiao, Haidong Shao, Jie Wang, et al.
Mechanical Systems and Signal Processing (2023) Vol. 207, pp. 110936-110936
Open Access | Times Cited: 103
A novel deep metric learning model for imbalanced fault diagnosis and toward open-set classification
Cunjun Wang, Cun Xin, Zili Xu
Knowledge-Based Systems (2021) Vol. 220, pp. 106925-106925
Closed Access | Times Cited: 101
CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery
Yadong Xu, Ke Feng, Xiaoan Yan, et al.
Information Fusion (2023) Vol. 95, pp. 1-16
Closed Access | Times Cited: 98
Physics-informed interpretable wavelet weight initialization and balanced dynamic adaptive threshold for intelligent fault diagnosis of rolling bearings
Chao He, Hongmei Shi, Jin Si, et al.
Journal of Manufacturing Systems (2023) Vol. 70, pp. 579-592
Closed Access | Times Cited: 58
TFN: An interpretable neural network with time-frequency transform embedded for intelligent fault diagnosis
Qian Chen, Xingjian Dong, Guowei Tu, et al.
Mechanical Systems and Signal Processing (2023) Vol. 207, pp. 110952-110952
Open Access | Times Cited: 56
An Intelligent Fault Diagnosis Method of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network
Qi Zhang, Linfeng Deng
Journal of Failure Analysis and Prevention (2023) Vol. 23, Iss. 2, pp. 795-811
Closed Access | Times Cited: 55
A multi-source domain information fusion network for rotating machinery fault diagnosis under variable operating conditions
Tianyu Gao, Jingli Yang, Qing Tang
Information Fusion (2024) Vol. 106, pp. 102278-102278
Closed Access | Times Cited: 46
Chatter detection in milling processes—a review on signal processing and condition classification
John Henry Navarro-Devia, Yun Chen, Dzung Viet Dao, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 125, Iss. 9-10, pp. 3943-3980
Open Access | Times Cited: 44
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