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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:

Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review
Xiao Zhang, Boyang Zhao, Yun Lin
IEEE Access (2021) Vol. 9, pp. 155598-155608
Open Access | Times Cited: 96

Showing 1-25 of 96 citing articles:

An improved GNN using dynamic graph embedding mechanism: A novel end-to-end framework for rolling bearing fault diagnosis under variable working conditions
Zidong Yu, Changhe Zhang, Chao Deng
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110534-110534
Closed Access | Times Cited: 54
A Survey on Fault Diagnosis of Rolling Bearings
Bo Peng, Ying Bi, Bing Xue, et al.
Algorithms (2022) Vol. 15, Iss. 10, pp. 347-347
Open Access | Times Cited: 55
A neural network compression method based on knowledge-distillation and parameter quantization for the bearing fault diagnosis
Mengyu Ji, Gaoliang Peng, Sijue Li, et al.
Applied Soft Computing (2022) Vol. 127, pp. 109331-109331
Closed Access | Times Cited: 46
Adversarial domain adaptation convolutional neural network for intelligent recognition of bearing faults
Yaochun Wu, Rongzhen Zhao, Hongru Ma, et al.
Measurement (2022) Vol. 195, pp. 111150-111150
Closed Access | Times Cited: 40
Research on rolling bearing virtual-real fusion life prediction with digital twin
Wentao Zhao, Chao Zhang, Bin Fan, et al.
Mechanical Systems and Signal Processing (2023) Vol. 198, pp. 110434-110434
Open Access | Times Cited: 35
Data-Driven Intelligent Condition Adaptation of Feature Extraction for Bearing Fault Detection Using Deep Responsible Active Learning
T R Mahesh, Saravanan Chandrasekaran, Veerma Ram, et al.
IEEE Access (2024) Vol. 12, pp. 45381-45397
Open Access | Times Cited: 12
Artificial Intelligence for safety and reliability: A descriptive, bibliometric and interpretative review on machine learning
Nicola Tamascelli, Alessandro Campari, Tarannom Parhizkar, et al.
Journal of Loss Prevention in the Process Industries (2024) Vol. 90, pp. 105343-105343
Open Access | Times Cited: 11
A novel robust intelligent fault diagnosis method for rolling bearings based on SPAVMD and WOA-LSSVM under noisy conditions
Xiaoan Yan, Xing Hua, Dong Jiang, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 5, pp. 056121-056121
Closed Access | Times Cited: 8
A novel stochastic resonance based deep residual network for fault diagnosis of rolling bearing system
Xuqun Zhang, Yumei Ma, Zhenkuan Pan, et al.
ISA Transactions (2024) Vol. 148, pp. 279-284
Closed Access | Times Cited: 8
Bearing Fault Diagnosis Based on VMD and Improved CNN
Zhenzhen Jin, Chen Diao, Deqiang He, et al.
Journal of Failure Analysis and Prevention (2022) Vol. 23, Iss. 1, pp. 165-175
Closed Access | Times Cited: 36
LSTM-Based Condition Monitoring and Fault Prognostics of Rolling Element Bearings Using Raw Vibrational Data
Yasir Saleem Afridi, Laiq Hasan, Rehmat Ullah, et al.
Machines (2023) Vol. 11, Iss. 5, pp. 531-531
Open Access | Times Cited: 19
Research on Digital Twin Driven Rolling Bearing Model-Data Fusion Life Prediction Method
Wentao Zhao, Chao Zhang, Jianguo Wang, et al.
IEEE Access (2023) Vol. 11, pp. 48611-48627
Open Access | Times Cited: 16
SKND-TSACNN: A novel time-scale adaptive CNN framework for fault diagnosis of rotating machinery
Zidong Yu, Changhe Zhang, Jie Liu, et al.
Knowledge-Based Systems (2023) Vol. 275, pp. 110682-110682
Closed Access | Times Cited: 16
A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilising MFPT Bearing Dataset
Shahd Hejazi, Michael Packianather, Ying Liu
Machines (2024) Vol. 12, Iss. 1, pp. 44-44
Open Access | Times Cited: 5
Optimization of Deep Belief Network Based on Sparrow Search Algorithm for Rolling Bearing Fault Diagnosis
Donghao Xu, Cheng Li
IEEE Access (2024) Vol. 12, pp. 10470-10481
Open Access | Times Cited: 5
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
Fault diagnosis using liquid state machine with spiking-timing-dependent plasticity learning rule
Yi Wan, Shaoping Wang, Di Liu
Expert Systems with Applications (2025) Vol. 271, pp. 126736-126736
Closed 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
Rolling bearing fault diagnosis method using time-frequency information integration and multi-scale TransFusion network
Zekun Wang, Zifei Xu, Chang Cai, et al.
Knowledge-Based Systems (2023) Vol. 284, pp. 111344-111344
Closed Access | Times Cited: 14
Magnetic rotor breakage study in permanent magnet synchronous motor at COMSOL multiphysics and fault detection using machine learning
Said Benkaihoul, Lakhdar Mazouz, Toufik Tayeb Naas, et al.
STUDIES IN ENGINEERING AND EXACT SCIENCES (2024) Vol. 5, Iss. 1, pp. 603-618
Open Access | Times Cited: 4
Deep Learning in Industrial Machinery: A Critical Review of Bearing Fault Classification Methods
Attiq Ur Rehman, Weidong Jiao, Yonghua Jiang, et al.
Applied Soft Computing (2025), pp. 112785-112785
Closed Access
Condition Monitoring of Electric Machines: Modern Frameworks and Data-Driven Methodologies
Wesley Doorsamy
Machines (2025) Vol. 13, Iss. 2, pp. 144-144
Open Access
A robust multi-scale learning network with quasi-hyperbolic momentum-based Adam optimizer for bearing intelligent fault diagnosis under sample imbalance scenarios and strong noise environment
Maoyou Ye, Xiaoan Yan, Ning Chen, et al.
Structural Health Monitoring (2023) Vol. 23, Iss. 3, pp. 1664-1686
Closed Access | Times Cited: 12
Fault diagnosis method for imbalanced data of rotating machinery based on time domain signal prediction and SC-ResNeSt
Haitao Wang, Yifan Guo, Xiang Liu, et al.
IEEE Access (2023) Vol. 11, pp. 38875-38893
Open Access | Times Cited: 10
ADAMS Simulation and HHT Feature Extraction Method for Bearing Faults of Coal Shearer
Yifan Qin, Xiang Fu, Xiaokun Li, et al.
Processes (2024) Vol. 12, Iss. 1, pp. 164-164
Open Access | Times Cited: 3
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