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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 Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings
Juan José Saucedo-Dorantes, Francisco Arellano-Espitia, Miguel Delgado-Prieto, et al.
Sensors (2021) Vol. 21, Iss. 17, pp. 5832-5832
Open Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

A Bearing Fault Diagnosis Method Based on Wavelet Packet Transform and Convolutional Neural Network Optimized by Simulated Annealing Algorithm
He Feng, Qing Ye
Sensors (2022) Vol. 22, Iss. 4, pp. 1410-1410
Open Access | Times Cited: 91
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
Improved SE-ResNet Acoustic–Vibration Fusion for Rolling Bearing Composite Fault Diagnosis
Xiaojiao Gu, Yang Tian, Chi Li, et al.
Applied Sciences (2024) Vol. 14, Iss. 5, pp. 2182-2182
Open Access | Times Cited: 9
Fault Detection of Wind Turbine Blades Using Multi-Channel CNN
Menghui Wang, Shiue‐Der Lu, Cheng-Che Hsieh, et al.
Sustainability (2022) Vol. 14, Iss. 3, pp. 1781-1781
Open Access | Times Cited: 35
The Bearing Faults Detection Methods for Electrical Machines—The State of the Art
Muhammad Amir Khan, Bilal Asad, Karolina Kudelina, et al.
Energies (2022) Vol. 16, Iss. 1, pp. 296-296
Open Access | Times Cited: 32
Deep-Learning Method Based on 1D Convolutional Neural Network for Intelligent Fault Diagnosis of Rotating Machines
Jorge Patricio Chuya Sumba, Luz María Alonso-Valerdi, David I. Ibarra-Zárate
Applied Sciences (2022) Vol. 12, Iss. 4, pp. 2158-2158
Open Access | Times Cited: 25
Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets
Yufeng Qin, Xianjun Shi
Applied Sciences (2022) Vol. 12, Iss. 17, pp. 8474-8474
Open Access | Times Cited: 24
Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis
Zahoor Ahmad, Tuan-Khai Nguyen, Sajjad Ahmad, et al.
Sensors (2021) Vol. 22, Iss. 1, pp. 179-179
Open Access | Times Cited: 31
Deep Transfer Learning Framework for Bearing Fault Detection in Motors
Prashant Kumar, P Kumar, Ananda Shankar Hati, et al.
Mathematics (2022) Vol. 10, Iss. 24, pp. 4683-4683
Open Access | Times Cited: 21
Intelligent Defect Diagnosis of Rolling Element Bearings under Variable Operating Conditions Using Convolutional Neural Network and Order Maps
Syed Muhammad Tayyab, Steven Chatterton, Paolo Pennacchi
Sensors (2022) Vol. 22, Iss. 5, pp. 2026-2026
Open Access | Times Cited: 19
Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM
Lei Yang, Yibo Jiang, Kang Zeng, et al.
Sensors (2024) Vol. 24, Iss. 10, pp. 2992-2992
Open Access | Times Cited: 3
Convolutional Neural Networks Based on Resonance Demodulation of Vibration Signal for Rolling Bearing Fault Diagnosis in Permanent Magnet Synchronous Motors
Ding Li, Haotian Guo, Liqiang Bian
Energies (2024) Vol. 17, Iss. 17, pp. 4334-4334
Open Access | Times Cited: 3
A Dual-Optimization Fault Diagnosis Method for Rolling Bearings Based on Hierarchical Slope Entropy and SVM Synergized with Shark Optimization Algorithm
Yuxing Li, Bingzhao Tang, Bo Huang, et al.
Sensors (2023) Vol. 23, Iss. 12, pp. 5630-5630
Open Access | Times Cited: 10
Multi-Scale Rolling Bearing Fault Diagnosis Method Based on Transfer Learning
Zhenyu Yin, Feiqing Zhang, Guangyuan Xu, et al.
Applied Sciences (2024) Vol. 14, Iss. 3, pp. 1198-1198
Open Access | Times Cited: 3
Bearing Fault Diagnosis Based on Parameter-Optimized Variational Mode Extraction and an Improved One-Dimensional Convolutional Neural Network
Rui Liang, Hanming Tao
Applied Sciences (2024) Vol. 14, Iss. 8, pp. 3289-3289
Open Access | Times Cited: 2
Vibration-Based Detection of Axlebox Bearing Considering Inner and Outer Ring Raceway Defects
Chuang Liu, Xinwen Zhang, Ruichen Wang, et al.
Lubricants (2024) Vol. 12, Iss. 5, pp. 142-142
Open Access | Times Cited: 2
Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model
Feifan Li, Zhuoheng Dai, Lei Jiang, et al.
Sensors (2024) Vol. 24, Iss. 21, pp. 6906-6906
Open Access | Times Cited: 2
Radial Magnetic Bearings for Rotor–Shaft Support in Electric Jet Engine
Krzysztof Falkowski, Paulina Kurnyta-Mazurek, Tomasz Szolc, et al.
Energies (2022) Vol. 15, Iss. 9, pp. 3339-3339
Open Access | Times Cited: 11
Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
Haihua Qin, Jiafang Pan, Jian Li, et al.
Applied Sciences (2023) Vol. 13, Iss. 13, pp. 7593-7593
Open Access | Times Cited: 5
Individual Feature Selection of Rolling Bearing Impedance Signals for Early Failure Detection
Florian Michael Becker-Dombrowsky, Quentin Sean Koplin, Eckhard Kirchner
Lubricants (2023) Vol. 11, Iss. 7, pp. 304-304
Open Access | Times Cited: 5
Fault Prediction of Rolling Element Bearings Using the Optimized MCKD–LSTM Model
Leilei Ma, Hong Jiang, Tongwei Ma, et al.
Machines (2022) Vol. 10, Iss. 5, pp. 342-342
Open Access | Times Cited: 8
Hybrid Feature Selection Framework for Bearing Fault Diagnosis Based on Wrapper-WPT
Andrei S. Maliuk, Zahoor Ahmad, Jong-Myon Kim
Machines (2022) Vol. 10, Iss. 12, pp. 1204-1204
Open Access | Times Cited: 8
Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
Guanwen Zhang, Dongnian Jiang
Applied Sciences (2024) Vol. 14, Iss. 3, pp. 1294-1294
Open Access | Times Cited: 1
A Comparative Analysis of Support Vector Machine and Decision Tree Algorithm for Predicting Fault in Uninterruptible Power Supply Systems
Isaac M. Doe, John Kojo Annan, Benjamin Odoi
International Journal of Innovative Technology and Exploring Engineering (2024) Vol. 13, Iss. 6, pp. 9-17
Closed Access | Times Cited: 1
Detection of Contamination and Failure in the Outer Race on Ceramic, Metallic, and Hybrid Bearings through AI Using Magnetic Flux and Current
Jonathan Cureño-Osornio, Geovanni Díaz-Saldaña, Roque A. Osornio‐Rios, et al.
Machines (2024) Vol. 12, Iss. 8, pp. 505-505
Open Access | Times Cited: 1
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