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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 Bearing Fault Diagnostics—A Comprehensive Review
Shen Zhang, Shibo Zhang, Bingnan Wang, et al.
IEEE Access (2020) Vol. 8, pp. 29857-29881
Open Access | Times Cited: 589

Showing 26-50 of 589 citing articles:

A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines
Sebastian Schwendemann, Zubair Amjad, Axel Sikora
Computers in Industry (2020) Vol. 125, pp. 103380-103380
Closed Access | Times Cited: 113
Role of artificial intelligence in rotor fault diagnosis: a comprehensive review
Aneesh G. Nath, Sandeep S. Udmale, Sanjay Kumar Singh
Artificial Intelligence Review (2020) Vol. 54, Iss. 4, pp. 2609-2668
Closed Access | Times Cited: 112
Multi-agent microgrid energy management based on deep learning forecaster
Mousa Afrasiabi, Mohammad Mohammadi, Mohammad Rastegar, et al.
Energy (2019) Vol. 186, pp. 115873-115873
Closed Access | Times Cited: 104
Semi-Supervised Bearing Fault Diagnosis and Classification Using Variational Autoencoder-Based Deep Generative Models
Shen Zhang, Fei Ye, Bingnan Wang, et al.
IEEE Sensors Journal (2020) Vol. 21, Iss. 5, pp. 6476-6486
Closed Access | Times Cited: 97
An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA
Melih Kuncan
IEEE Access (2020) Vol. 8, pp. 137517-137529
Open Access | Times Cited: 91
Bearing fault diagnosis with intermediate domain based Layered Maximum Mean Discrepancy: A new transfer learning approach
Sebastian Schwendemann, Zubair Amjad, Axel Sikora
Engineering Applications of Artificial Intelligence (2021) Vol. 105, pp. 104415-104415
Closed Access | Times Cited: 91
Applying machine learning approach in recycling
Merve Erkınay Özdemir, Zaara Ali, Balakrishnan Subeshan, et al.
Journal of Material Cycles and Waste Management (2021) Vol. 23, Iss. 3, pp. 855-871
Closed Access | Times Cited: 88
Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning
Shen Zhang, Fei Ye, Bingnan Wang, et al.
IEEE Transactions on Industry Applications (2021) Vol. 57, Iss. 5, pp. 4754-4764
Open Access | Times Cited: 82
Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls
Carla Barros, Carlos A. Silva, Ana P. Pinheiro
Artificial Intelligence in Medicine (2021) Vol. 114, pp. 102039-102039
Open Access | Times Cited: 79
A study on adaptation lightweight architecture based deep learning models for bearing fault diagnosis under varying working conditions
Jie Wu, Tang Tang, Ming Chen, et al.
Expert Systems with Applications (2020) Vol. 160, pp. 113710-113710
Closed Access | Times Cited: 72
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: 71
Deep multiple auto-encoder with attention mechanism network: A dynamic domain adaptation method for rotary machine fault diagnosis under different working conditions
Shengkang Yang, Xianguang Kong, Qibin Wang, et al.
Knowledge-Based Systems (2022) Vol. 249, pp. 108639-108639
Closed Access | Times Cited: 63
Enhanced Discriminate Feature Learning Deep Residual CNN for Multitask Bearing Fault Diagnosis With Information Fusion
Guangxing Niu, Enhui Liu, Xuan Wang, et al.
IEEE Transactions on Industrial Informatics (2022) Vol. 19, Iss. 1, pp. 762-770
Closed Access | Times Cited: 61
A Novel Fault Diagnosis Method of Rolling Bearing Based on Integrated Vision Transformer Model
Xinyu Tang, Zengbing Xu, Zhigang Wang
Sensors (2022) Vol. 22, Iss. 10, pp. 3878-3878
Open Access | Times Cited: 53
Intelligent technologies for construction machinery using data-driven methods
Zhe Zheng, Fei Wang, Guofang Gong, et al.
Automation in Construction (2022) Vol. 147, pp. 104711-104711
Closed Access | Times Cited: 52
A Systematic Guide for Predicting Remaining Useful Life with Machine Learning
Tarek Berghout, Mohamed Benbouzid
Electronics (2022) Vol. 11, Iss. 7, pp. 1125-1125
Open Access | Times Cited: 50
A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles
Guangxi Yan, Chen Jiang, Yu Bai, et al.
Processes (2022) Vol. 10, Iss. 4, pp. 724-724
Open Access | Times Cited: 43
Early Detection of Faults in Induction Motors—A Review
Tomas Alberto Garcia-Calva, Daniel Moríñigo-Sotelo, Vanesa Fernandez-Cavero, et al.
Energies (2022) Vol. 15, Iss. 21, pp. 7855-7855
Open Access | Times Cited: 41
Physics-informed machine learning in prognostics and health management: State of the art and challenges
Weikun Deng, Khanh T.P. Nguyen, Kamal Medjaher, et al.
Applied Mathematical Modelling (2023) Vol. 124, pp. 325-352
Open Access | Times Cited: 35
One-Dimensional LSTM-Regulated Deep Residual Network for Data-Driven Fault Detection in Electric Machines
Arta Mohammad‐Alikhani, Babak Nahid‐Mobarakeh, Min‐Fu Hsieh
IEEE Transactions on Industrial Electronics (2023) Vol. 71, Iss. 3, pp. 3083-3092
Closed Access | Times Cited: 33
Bearing Fault Diagnosis With Envelope Analysis and Machine Learning Approaches Using CWRU Dataset
M. Alonso-González, Vicente García‐Díaz, Benjamín López Pérez, et al.
IEEE Access (2023) Vol. 11, pp. 57796-57805
Open Access | Times Cited: 33
Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends
Shen Zhang, Oliver Wallscheid, Mario Porrmann
IEEE Open Journal of Industry Applications (2023) Vol. 4, pp. 188-214
Open Access | Times Cited: 31
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: 30
A novel method for bearing fault diagnosis based on BiLSTM neural networks
Saadi Mohamed Nacer, Bouteraa Nadia, Abdelghani Redjati, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 125, Iss. 3-4, pp. 1477-1492
Closed Access | Times Cited: 27
A comprehensive review on self-powered smart bearings
Ying Zhang, Wei Wang, Xin Wu, et al.
Renewable and Sustainable Energy Reviews (2023) Vol. 183, pp. 113446-113446
Closed Access | Times Cited: 24

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