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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:

Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery
Jiantao Lu, Weiwei Qian, Shunming Li, et al.
Applied Sciences (2021) Vol. 11, Iss. 3, pp. 919-919
Open Access | Times Cited: 91

Showing 1-25 of 91 citing articles:

A Review of Data-Driven Machinery Fault Diagnosis Using Machine Learning Algorithms
Jian Cen, Zhuohong Yang, Xi Liu, et al.
Journal of Vibration Engineering & Technologies (2022) Vol. 10, Iss. 7, pp. 2481-2507
Closed Access | Times Cited: 88
Thermographic fault diagnosis of electrical faults of commutator and induction motors
Adam Głowacz
Engineering Applications of Artificial Intelligence (2023) Vol. 121, pp. 105962-105962
Closed Access | Times Cited: 81
Spatial graph convolutional neural network via structured subdomain adaptation and domain adversarial learning for bearing fault diagnosis
Mohammadreza Ghorvei, Mohammadreza Kavianpour, Mohammad Taghi Hamidi Beheshti, et al.
Neurocomputing (2022) Vol. 517, pp. 44-61
Open Access | Times Cited: 77
Rolling bearing fault diagnosis based on 2D time-frequency images and data augmentation technique
Wenlong Fu, Xiaohui Jiang, Bailin Li, et al.
Measurement Science and Technology (2022) Vol. 34, Iss. 4, pp. 045005-045005
Closed Access | Times Cited: 72
A class alignment method based on graph convolution neural network for bearing fault diagnosis in presence of missing data and changing working conditions
Mohammadreza Kavianpour, Amin Ramezani, Mohammad Taghi Hamidi Beheshti
Measurement (2022) Vol. 199, pp. 111536-111536
Closed Access | Times Cited: 41
A physics-informed feature weighting method for bearing fault diagnostics
Hao Lü, Venkat Pavan Nemani, Vahid Barzegar, et al.
Mechanical Systems and Signal Processing (2023) Vol. 191, pp. 110171-110171
Open Access | Times Cited: 34
Non-negative wavelet matrix factorization-based bearing fault intelligent classification method
Zhilin Dong, Dezun Zhao, Lingli Cui
Measurement Science and Technology (2023) Vol. 34, Iss. 11, pp. 115013-115013
Closed Access | Times Cited: 24
Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis
Bakr Ahmed Taha, Naser M. Ahmed, Rishi Kumar Talreja, et al.
ACS Synthetic Biology (2024) Vol. 13, Iss. 6, pp. 1600-1620
Closed Access | Times Cited: 13
A roadmap to fault diagnosis of industrial machines via machine learning: A brief review
Govind Vashishtha, Sumika Chauhan, Mert Sehri, et al.
Measurement (2024) Vol. 242, pp. 116216-116216
Closed Access | Times Cited: 10
Ensemble empirical mode decomposition energy moment entropy and enhanced long short-term memory for early fault prediction of bearing
Zehai Gao, Yang Liu, Quanjiu Wang, et al.
Measurement (2021) Vol. 188, pp. 110417-110417
Closed Access | Times Cited: 44
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
Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system
Yuguo Li, Jian Liu, Qichao Zhou, et al.
Advanced Engineering Informatics (2022) Vol. 53, pp. 101666-101666
Closed Access | Times Cited: 33
High-Level K-Nearest Neighbors (HLKNN): A Supervised Machine Learning Model for Classification Analysis
Elife Ozturk Kiyak, Bita Ghasemkhani, Derya Birant
Electronics (2023) Vol. 12, Iss. 18, pp. 3828-3828
Open Access | Times Cited: 20
Research on fault diagnosis of supercharged boiler with limited data based on few-shot learning
Guolong Li, Yanjun Li, Chengyue Fang, et al.
Energy (2023) Vol. 281, pp. 128286-128286
Closed Access | Times Cited: 17
Fault diagnosis of marine electric thruster bearing based on fusing multi-sensor deep learning models
Xueqin Zhang, Chenxing Sheng, Wu Ouyang, et al.
Measurement (2023) Vol. 214, pp. 112727-112727
Closed Access | Times Cited: 16
A fault diagnosis method for nuclear power plant rotating machinery based on adaptive deep feature extraction and multiple support vector machines
Wenzhe Yin, Hong Xia, Xueying Huang, et al.
Progress in Nuclear Energy (2023) Vol. 164, pp. 104862-104862
Closed Access | Times Cited: 16
Enhancing Rolling Bearing Fault Diagnosis in Motors using the OCSSA-VMD-CNN-BiLSTM Model: A Novel Approach for Fast and Accurate Identification
Chang Yong, Guangqing Bao
IEEE Access (2024) Vol. 12, pp. 78463-78479
Open Access | Times Cited: 7
Domain adaptation with domain specific information and feature disentanglement for bearing fault diagnosis
Shaozhang Xie, Peng Xia, Hanqi Zhang
Measurement Science and Technology (2024) Vol. 35, Iss. 5, pp. 056101-056101
Open Access | Times Cited: 6
On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm
Przemysław Pietrzak, Marcin Wolkiewicz
Electronics (2021) Vol. 10, Iss. 15, pp. 1786-1786
Open Access | Times Cited: 39
An unsupervised bearing fault diagnosis based on deep subdomain adaptation under noise and variable load condition
Mohammadreza Ghorvei, Mohammadreza Kavianpour, Mohammad Taghi Hamidi Beheshti, et al.
Measurement Science and Technology (2021) Vol. 33, Iss. 2, pp. 025901-025901
Closed Access | Times Cited: 39
Predictive Monitoring of Incipient Faults in Rotating Machinery: A Systematic Review from Data Acquisition to Artificial Intelligence
Kanika Saini, S. S. Dhami, Vanraj
Archives of Computational Methods in Engineering (2022) Vol. 29, Iss. 6, pp. 4005-4026
Closed Access | Times Cited: 23
A novel convolutional neural network with multiscale cascade midpoint residual for fault diagnosis of rolling bearings
Zhiqiang Chao, Tian Han
Neurocomputing (2022) Vol. 506, pp. 213-227
Closed Access | Times Cited: 22
An Efficient Sequential Embedding ConvNet for Rotating Machinery Intelligent Fault Diagnosis
Jian Tang, Qǐháng Wú, Xiaobo Li, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-13
Closed Access | Times Cited: 13
A prior knowledge-enhanced self-supervised learning framework using time-frequency invariance for machinery intelligent fault diagnosis with small samples
Jian Tang, Jiawei Xiao, Wentao Chen, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108503-108503
Closed Access | Times Cited: 4
Fault Diagnosis for Supercharged Boiler Based on Self-improving Few-Shot Learning
Guolong Li, Yanjun Li, Jian Su, et al.
Energy (2025), pp. 134507-134507
Closed Access
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