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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.

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Showing 1-25 of 60 citing articles:

A comprehensive review on convolutional neural network in machine fault diagnosis
Jinyang Jiao, Ming Zhao, Jing Lin, et al.
Neurocomputing (2020) Vol. 417, pp. 36-63
Open Access | Times Cited: 407
Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review
Dhiraj Neupane, Jongwon Seok
IEEE Access (2020) Vol. 8, pp. 93155-93178
Open Access | Times Cited: 373
A systematic review of deep transfer learning for machinery fault diagnosis
Chuan Li, Shaohui Zhang, Qin Yi, et al.
Neurocomputing (2020) Vol. 407, pp. 121-135
Closed Access | Times Cited: 348
Intelligent Maintenance Systems and Predictive Manufacturing
Jay Lee, Jun Ni, Jaskaran Singh, et al.
Journal of Manufacturing Science and Engineering (2020) Vol. 142, Iss. 11
Closed Access | Times Cited: 124
Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms
Rafia Nishat Toma, Jong-Myon Kim
Applied Sciences (2020) Vol. 10, Iss. 15, pp. 5251-5251
Open Access | Times Cited: 99
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: 72
PMSM Stator Winding Fault Detection and Classification Based on Bispectrum Analysis and Convolutional Neural Network
Przemysław Pietrzak, Marcin Wolkiewicz, Teresa Orłowska-Kowalska
IEEE Transactions on Industrial Electronics (2022) Vol. 70, Iss. 5, pp. 5192-5202
Closed Access | Times Cited: 45
Deep learning-based cross-domain adaptation for gearbox fault diagnosis under variable speed conditions
Jaskaran Singh, Moslem Azamfar, Abhijeet Ainapure, et al.
Measurement Science and Technology (2019) Vol. 31, Iss. 5, pp. 055601-055601
Closed Access | Times Cited: 71
A systematic review of machine learning algorithms for prognostics and health management of rolling element bearings: fundamentals, concepts and applications
Jaskaran Singh, Moslem Azamfar, Fei Li, et al.
Measurement Science and Technology (2020) Vol. 32, Iss. 1, pp. 012001-012001
Closed Access | Times Cited: 65
Hierarchical diagnosis of bearing faults using branch convolutional neural network considering noise interference and variable working conditions
Kaige Su, Jianhua Liu, Hui Xiong
Knowledge-Based Systems (2021) Vol. 230, pp. 107386-107386
Closed Access | Times Cited: 49
A novel deep learning approach for intelligent fault diagnosis applications based on time-frequency images
Özgür Gültekin, E. Miné Cinar, Kemal Özkan, et al.
Neural Computing and Applications (2022) Vol. 34, Iss. 6, pp. 4803-4812
Closed Access | Times Cited: 33
Integrated Gradient-Based Continuous Wavelet Transform for Bearing Fault Diagnosis
Junfei Du, Xinyu Li, Yiping Gao, et al.
Sensors (2022) Vol. 22, Iss. 22, pp. 8760-8760
Open Access | Times Cited: 32
The Method of Rolling Bearing Fault Diagnosis Based on Multi-Domain Supervised Learning of Convolution Neural Network
Xuejun Liu, Wei Sun, Hongkun Li, et al.
Energies (2022) Vol. 15, Iss. 13, pp. 4614-4614
Open Access | Times Cited: 29
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: 17
Rotor Fault Diagnosis Method Using CNN-Based Transfer Learning with 2D Sound Spectrogram Analysis
Haiyoung Jung, Sugi Choi, Bohee Lee
Electronics (2023) Vol. 12, Iss. 3, pp. 480-480
Open Access | Times Cited: 16
Intelligent Diagnostics for Ball Screw Fault Through Indirect Sensing Using Deep Domain Adaptation
Vibhor Pandhare, Xiang Li, Marcella Miller, et al.
IEEE Transactions on Instrumentation and Measurement (2020) Vol. 70, pp. 1-11
Closed Access | Times Cited: 40
A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks
Daoguang Yang, Hamid Reza Karimi, Len Gelman
Sensors (2022) Vol. 22, Iss. 2, pp. 671-671
Open Access | Times Cited: 23
Integrating vibration signal analysis and image embedding for enhanced bearing fault diagnosis in manufacturing
Yongmin Kim, Hyunsoo Yoon
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Homomorphic encryption-based fault diagnosis in IoT-enabled industrial systems
Hoki Kim, Youngdoo Son, Junyoung Byun
International Journal of Information Security (2025) Vol. 24, Iss. 3
Closed Access
Explainable Fault Classification and Severity Diagnosis in Rotating Machinery Using Kolmogorov–Arnold Networks
Spyros Rigas, Michalis Papachristou, Ioannis Nektarios Sotiropoulos, et al.
Entropy (2025) Vol. 27, Iss. 4, pp. 403-403
Open Access
Prognostics Analysis of Rolling Bearing Based on Bi-Directional LSTM and Attention Mechanism
Maan Singh Rathore, S. P. Harsha
Journal of Failure Analysis and Prevention (2022) Vol. 22, Iss. 2, pp. 704-723
Closed Access | Times Cited: 20
A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions
Rafia Nishat Toma, Farzin Piltan, Kichang Im, et al.
Sensors (2022) Vol. 22, Iss. 13, pp. 4881-4881
Open Access | Times Cited: 20
Vibration Image Representations for Fault Diagnosis of Rotating Machines: A Review
Hosameldin Ahmed, Asoke K. Nandi
Machines (2022) Vol. 10, Iss. 12, pp. 1113-1113
Open Access | Times Cited: 19
Performance analysis of genetically optimized 1D-convolutional neural network architecture for rotor system fault detection and diagnosis
Sudhar Rajagopalan, Jaskaran Singh, Ashish Purohit
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (2024)
Closed Access | Times Cited: 3
A Study of Noise Effect in Electrical Machines Bearing Fault Detection and Diagnosis Considering Different Representative Feature Models
Dimitrios A. Moysidis, Georgios D. Karatzinis, Yiannis S. Boutalis, et al.
Machines (2023) Vol. 11, Iss. 11, pp. 1029-1029
Open Access | Times Cited: 8
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