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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:

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

Showing 1-25 of 71 citing articles:

Improved Fault Classification for Predictive Maintenance in Industrial IoT Based on AutoML: A Case Study of Ball-Bearing Faults
Russul H. Hadi, Haider Najy Hady, Ahmed Mudheher Hasan, et al.
Processes (2023) Vol. 11, Iss. 5, pp. 1507-1507
Open Access | Times Cited: 51
A multi-sensor fused incremental broad learning with D-S theory for online fault diagnosis of rotating machinery
Xuefang Xu, Shuo Bao, Haidong Shao, et al.
Advanced Engineering Informatics (2024) Vol. 60, pp. 102419-102419
Closed Access | Times Cited: 15
A broad learning model guided by global and local receptive causal features for online incremental machinery fault diagnosis
Xuefang Xu, Shuo Bao, Pengfei Liang, et al.
Expert Systems with Applications (2024) Vol. 246, pp. 123124-123124
Closed Access | Times Cited: 10
CNC Machine-Bearing Fault Detection Based on Convolutional Neural Network Using Vibration and Acoustic Signal
Mohmad Iqbal, Ashok Kumar Madan
Journal of Vibration Engineering & Technologies (2022) Vol. 10, Iss. 5, pp. 1613-1621
Closed Access | Times Cited: 34
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
A Novel Framework for Robust Bearing Fault Diagnosis: Preprocessing, Model Selection, and Performance Evaluation
Faisal Althobiani
IEEE Access (2024) Vol. 12, pp. 59018-59036
Open Access | Times Cited: 5
Physics‐informed anomaly and fault detection for wind energy systems using deep CNN and adaptive elite PSO‐XGBoost
Chun‐Yao Lee, Edu Daryl C. Maceren
IET Generation Transmission & Distribution (2025) Vol. 19, Iss. 1
Open Access
Deep learning based approaches for intelligent industrial machinery health management and fault diagnosis in resource-constrained environments
Ali Saeed, Muazzam A. Khan, Usman Akram, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Predictive Health Monitoring of Induction Motors Using 1D Convolutional Neural Network
D. Suganya, R. Rajavel, A.K. Lakshminarayanan
Journal of Vibration Engineering & Technologies (2025) Vol. 13, Iss. 1
Closed Access
Design and Implementation of a Deep Learning-Based Hand Gesture Recognition System for Rehabilitation Internet-of-Things (RIoT) Environments Using MediaPipe
Nurul Hanis Mohd Dhuzuki, Ahmad Anwar Zainuddin, Nur Anis Sofea Kamarul Zaman, et al.
IIUM Engineering Journal (2025) Vol. 26, Iss. 1, pp. 353-372
Open Access
An Analytical Benchmark of Feature Selection Techniques for Industrial Fault Classification Leveraging Time-Domain Features
Meltem Süpürtülü, Ayşenur Hatipoğlu, Ersen Yılmaz
Applied Sciences (2025) Vol. 15, Iss. 3, pp. 1457-1457
Open Access
OrbitNet: A new CNN model for automatic fault diagnostics of turbomachines
Xiaomo Jiang, Shuhua Yang, Fumin Wang, et al.
Applied Soft Computing (2021) Vol. 110, pp. 107702-107702
Closed Access | Times Cited: 34
Influence of One-Way ANOVA and Kruskal–Wallis Based Feature Ranking on the Performance of ML Classifiers for Bearing Fault Diagnosis
Mohd Atif Jamil, Sidra Khanam
Journal of Vibration Engineering & Technologies (2023) Vol. 12, Iss. 3, pp. 3101-3132
Closed Access | Times Cited: 15
FaultFormer: Pretraining Transformers for Adaptable Bearing Fault Classification
Anthony Y. Zhou, Amir Barati Farimani
IEEE Access (2024) Vol. 12, pp. 70719-70728
Open Access | Times Cited: 4
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
Low-Power Detection and Classification for In-Sensor Predictive Maintenance Based on Vibration Monitoring
Paola Vitolo, Antonio De Vita, Luigi Di Benedetto, et al.
IEEE Sensors Journal (2022) Vol. 22, Iss. 7, pp. 6942-6951
Open Access | Times Cited: 19
Bearing Fault Diagnosis Based on Multi-Scale CNN and Bidirectional GRU
Taher Saghi, Danyal Bustan, Sumeet S. Aphale
Vibration (2022) Vol. 6, Iss. 1, pp. 11-28
Open Access | Times Cited: 18
Impact of Deep Learning Optimizers and Hyperparameter Tuning on the Performance of Bearing Fault Diagnosis
Seongjae Lee, Taehyoun Kim
IEEE Access (2023) Vol. 11, pp. 55046-55070
Open Access | Times Cited: 9
A global interactive attention-based lightweight denoising network for locating internal defects of CFRP laminates
Bo Yang, Yang Zhang, Shilong Wang, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 116, pp. 105436-105436
Closed Access | Times Cited: 14
A Multi-Scale Attention Mechanism Based Domain Adversarial Neural Network Strategy for Bearing Fault Diagnosis
Quanling Zhang, Ningze Tang, Xing Fu, et al.
Actuators (2023) Vol. 12, Iss. 5, pp. 188-188
Open Access | Times Cited: 8
A dynamic-inner convolutional autoencoder for process monitoring
Shuyuan Zhang, Tong Qiu
Computers & Chemical Engineering (2021) Vol. 158, pp. 107654-107654
Closed Access | Times Cited: 18
Machine tool fault classification diagnosis based on audio parameters
Shaohu Ding, Sen Zhang, Chenchen Yang
Results in Engineering (2023) Vol. 19, pp. 101308-101308
Open Access | Times Cited: 7
Wind energy system fault classification using deep CNN and improved PSO‐tuned extreme gradient boosting
Chun‐Yao Lee, Edu Daryl C. Maceren
IET Renewable Power Generation (2024)
Open Access | Times Cited: 2
Research on fault diagnosis of rolling bearing based on multi-sensor bi-layer information fusion under small samples
Chaoqun Hu, Yonghua Li, Zhe Chen, et al.
Review of Scientific Instruments (2023) Vol. 94, Iss. 11
Closed Access | Times Cited: 5
CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis
Ching-Che Chung, Yu-Pei Liang, Jiang HongJin
Sensors (2023) Vol. 23, Iss. 13, pp. 5897-5897
Open Access | Times Cited: 4
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