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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:

Diagnosis for Slight Bearing Fault in Induction Motor Based on Combination of Selective Features and Machine Learning
Hisahide Nakamura, Yukio Mizuno
Energies (2022) Vol. 15, Iss. 2, pp. 453-453
Open Access | Times Cited: 22

Showing 22 citing articles:

Common Diagnosis Approach to Three-Class Induction Motor Faults Using Stator Current Feature and Support Vector Machine
Kenichi Yatsugi, Shrinathan Esakimuthu Pandarakone, Yukio Mizuno, et al.
IEEE Access (2023) Vol. 11, pp. 24945-24952
Open Access | Times Cited: 22
Based on spatial Mahalanobis distance: A novel zero-shot learning method for compound fault identification and decoupling
Miao Jiang, Yang Xiang, Chenxing Sheng
Expert Systems with Applications (2025) Vol. 272, pp. 126680-126680
Closed Access
A 2DCNN-RF Model for Offshore Wind Turbine High-Speed Bearing-Fault Diagnosis under Noisy Environment
Shujie Yang, Peikun Yang, Hao Yu, et al.
Energies (2022) Vol. 15, Iss. 9, pp. 3340-3340
Open Access | Times Cited: 26
Spectral proper orthogonal decomposition and machine learning algorithms for bearing fault diagnosis
Adel Afia, Fawzi Gougam, Walid Touzout, et al.
Journal of the Brazilian Society of Mechanical Sciences and Engineering (2023) Vol. 45, Iss. 10
Closed Access | Times Cited: 14
A Machine Learning Algorithm to Aid the Development of Repair Materials for Ancient Ceramics via Additive Manufacturing
Jian‐Hong Ye
Processes (2025) Vol. 13, Iss. 1, pp. 145-145
Open Access
Bearing fault diagnosis method based on angular domain resampling, relative position matrix and transfer learning
Xun Zhang, Guanghua Xu, Xiaobi Chen, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 7, pp. 076118-076118
Closed Access | Times Cited: 3
Machine Learning Approach for Predicting the Solid Particle Lubricant Contamination in a Spherical Roller Bearing
K. Rameshkumar, Kaviarasu Nataraj, P. Krishnakumar, et al.
IEEE Access (2024) Vol. 12, pp. 78680-78700
Open Access | Times Cited: 2
Gearbox Fault Diagnosis Using REMD, EO and Machine Learning Classifiers
Adel Afia, Fawzi Gougam, Chemseddine Rahmoune, et al.
Journal of Vibration Engineering & Technologies (2023) Vol. 12, Iss. 3, pp. 4673-4697
Closed Access | Times Cited: 5
Bearing Failure Analysis Using Vibration Analysis and Natural Frequency Excitation
Ali Hemati, Alireza Shooshtari
Journal of Failure Analysis and Prevention (2023) Vol. 23, Iss. 4, pp. 1431-1437
Closed Access | Times Cited: 5
Research on online fault detection tool of substation equipment based on artificial intelligence
Cheng Xingxin, Zheng Xin, Wu Gangming
Journal of King Saud University - Science (2022) Vol. 34, Iss. 6, pp. 102149-102149
Open Access | Times Cited: 8
Bearing Faults Diagnosis by Current Envelope Analysis under Direct Torque Control Based on Neural Networks and Fuzzy Logic—A Comparative Study
Abderrahman El Idrissi, Aziz Derouich, Said Mahfoud, et al.
Electronics (2024) Vol. 13, Iss. 16, pp. 3195-3195
Open Access | Times Cited: 1
A Zoomed Root-Prony Technique for Efficient Bearing Fault Detection in Induction Motors
Mohamed Abdeldjabbar Kouadria, Zakaria Chedjara, Mohamed Benbouzid, et al.
Results in Engineering (2024) Vol. 24, pp. 103367-103367
Open Access | Times Cited: 1
Generalized Roughness Bearing Fault Diagnosis Using Time Series Analysis and Gradient Boosted Tree
Mojtaba Afshar, Mehrdad Heydarzadeh, Bilal Akin
2022 IEEE Energy Conversion Congress and Exposition (ECCE) (2022), pp. 1-4
Closed Access | Times Cited: 6
Motor Current Signature Analysis for Detecting Local Defects on Rolling-Element Bearings of Induction Motors
Mehdi Tabasi, Mohammad Mostafavi, Mansour Ojaghi
Arabian Journal for Science and Engineering (2023) Vol. 48, Iss. 11, pp. 14811-14822
Closed Access | Times Cited: 3
Fault Diagnosis of Motor Bearing Based on Current Bi-Spectrum and Convolutional Neural Network
Jiaojiao Ma, Lingli Jiang, Shuhui Li, et al.
Latin American Journal of Solids and Structures (2023) Vol. 20, Iss. 5
Open Access | Times Cited: 3
A Dilated Convolution Neural Network for Gear Fault Diagnosis
Bin Han
(2023), pp. 1506-1510
Closed Access | Times Cited: 2
Application of Convolutional Neural Network for Fault Diagnosis of Bearing Scratch of an Induction Motor
Shrinathan Esaki Muthu Pandara Kone, Kenichi Yatsugi, Yukio Mizuno, et al.
Applied Sciences (2022) Vol. 12, Iss. 11, pp. 5513-5513
Open Access | Times Cited: 3
Opportunities and challenges in the modeling, experimental investigation, and development of foil bearings for ultra-high-speed rotating machinery
Hanqing Guan, J. Li, Kai Feng
Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology (2024)
Closed Access
Bearing Scratch Fault Detection by Three‐Dimensional Features and a Support Vector Machine
Kenichi Yatsugi, Shrinathan Esaki Muthu Pandara Kone, Yukio Mizuno, et al.
IEEJ Transactions on Electrical and Electronic Engineering (2022) Vol. 18, Iss. 3, pp. 470-476
Closed Access | Times Cited: 2
Faulty Class Diagnosis of Three Phase Induction Motor Bearing Using Stator Current Spectral Features and Machine Learning Algorithms
Kenichi Yatsugi, Shrinathan Esaki Muthu Pandara Kone, Yukio Mizuno
2022 9th International Conference on Condition Monitoring and Diagnosis (CMD) (2022), pp. 112-117
Closed Access | Times Cited: 1
Intelligent Fault Diagnosis of Electronic Engineering Equipment System Based on Machine Learning Algorithm
Yan Yang
Lecture notes in mechanical engineering (2023), pp. 823-833
Closed Access
Bearing Fault Detection in Induction Motor using Ensemble Learning
Tejas B. Sawai, Suryakant Mudke, Preeti V. Kapoor, et al.
(2023), pp. 1-6
Closed Access
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