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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:

Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging
Xin Li, Yong Li, Ke Yan, et al.
Reliability Engineering & System Safety (2022) Vol. 230, pp. 108921-108921
Closed Access | Times Cited: 36

Showing 1-25 of 36 citing articles:

A novel digital twin-driven approach based on physical-virtual data fusion for gearbox fault diagnosis
Jingyan Xia, Ruyi Huang, Zhuyun Chen, et al.
Reliability Engineering & System Safety (2023) Vol. 240, pp. 109542-109542
Closed Access | Times Cited: 50
A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern
Pengcheng Xia, Yixiang Huang, Zhiyu Tao, et al.
Reliability Engineering & System Safety (2023) Vol. 235, pp. 109256-109256
Closed Access | Times Cited: 42
Rolling Bearing Fault Diagnosis Under Data Imbalance and Variable Speed Based on Adaptive Clustering Weighted Oversampling
Sai Li, Yanfeng Peng, Yiping Shen, et al.
Reliability Engineering & System Safety (2024) Vol. 244, pp. 109938-109938
Closed Access | Times Cited: 42
Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns
Jiawei Xiong, Jian Zhou, Yizhong Ma, et al.
Reliability Engineering & System Safety (2023) Vol. 235, pp. 109244-109244
Closed Access | Times Cited: 32
Semi-supervised ensemble fault diagnosis method based on adversarial decoupled auto-encoder with extremely limited labels
Congying Deng, Zihao Deng, Jianguo Miao
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109740-109740
Closed Access | Times Cited: 23
Intelligent fault identification in sample imbalance scenarios using robust low-rank matrix classifier with fuzzy weighting factor
Haifeng Xu, Haiyang Pan, Jinde Zheng, et al.
Applied Soft Computing (2024) Vol. 152, pp. 111229-111229
Closed Access | Times Cited: 9
Semi-supervised fault diagnosis of wheelset bearings in high-speed trains using autocorrelation and improved flow Gaussian mixture model
Jiayi Wu, Yilei Li, Limin Jia, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 132, pp. 107861-107861
Closed Access | Times Cited: 8
A Review of Statistical-Based Fault Detection and Diagnosis with Probabilistic Models
Yanting Zhu, Shunyi Zhao, Yuxuan Zhang, et al.
Symmetry (2024) Vol. 16, Iss. 4, pp. 455-455
Open Access | Times Cited: 8
Semi-supervised small sample fault diagnosis under a wide range of speed variation conditions based on uncertainty analysis
Dawei Gao, Kai Huang, Yongsheng Zhu, et al.
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109746-109746
Closed Access | Times Cited: 19
A Semi-supervised Gaussian Mixture Variational Autoencoder method for few-shot fine-grained fault diagnosis
Zhiqian Zhao, Yeyin Xu, Jiabin Zhang, et al.
Neural Networks (2024) Vol. 178, pp. 106482-106482
Closed Access | Times Cited: 5
Scraper conveyor gearbox fault diagnosis based on multi-source heterogeneous data fusion
Long Feng, Zeyu Ding, Yibing Yin, et al.
Measurement (2025), pp. 116797-116797
Closed Access
A Transformer-based self-supervised learning model for fault diagnosis of air-conditioning systems with limited labeled data
Hua Mei, Ke Yan, Xin Li
Engineering Applications of Artificial Intelligence (2025) Vol. 146, pp. 110331-110331
Closed Access
A multi-branch attention coupled convolutional domain adaptation network for bearing intelligent fault recognition under unlabeled sample scenarios
Maoyou Ye, Xiaoan Yan, Dong Jiang, et al.
Applied Soft Computing (2025), pp. 113053-113053
Closed Access
Deep feature interactive network for machinery fault diagnosis using multi-source heterogeneous data
Mengqi Miao, Jianbo Yu
Reliability Engineering & System Safety (2023) Vol. 242, pp. 109795-109795
Closed Access | Times Cited: 14
Using machine learning approach to investigate the impact of missing sensors on fault classification of multistage gear box
S V V S Narayana Pichika, Brahmini Priya Venkata Pragada, G. R. Sabareesh, et al.
Journal of the Brazilian Society of Mechanical Sciences and Engineering (2025) Vol. 47, Iss. 3
Closed Access
Image texture feature fusion enhancement for bearing fault diagnosis based on maximum gradient
Yongjian Sun, Gang Yu, Wei Wang
Reliability Engineering & System Safety (2025), pp. 111009-111009
Closed Access
Reweighted periodic overlapping group lasso for impulsive feature extraction and its application to spiral bevel gear local fault diagnosis
Keyuan Li, Junjiang Liu, Zhibin Zhao, et al.
Mechanical Systems and Signal Processing (2025) Vol. 230, pp. 112572-112572
Closed Access
A novel fault diagnosis framework for rotating machinery with hierarchical multiscale symbolic diversity entropy and robust twin hyperdisk-based tensor machine
Zuanyu Zhu, Junsheng Cheng, Ping Wang, et al.
Reliability Engineering & System Safety (2022) Vol. 231, pp. 109037-109037
Closed Access | Times Cited: 18
Dynamics simulation-driven fault diagnosis of rolling bearings using security transfer support matrix machine
Xin Li, Shuhua Li, Dong Wei, et al.
Reliability Engineering & System Safety (2023) Vol. 243, pp. 109882-109882
Closed Access | Times Cited: 10
DNOCNet: A Novel End-to-End Network for Induction Motor Drive Systems Fault Diagnosis Under Speed Fluctuation Condition
Junchao Guo, Qingbo He, Fengshou Gu
IEEE Transactions on Industrial Informatics (2024) Vol. 20, Iss. 6, pp. 8284-8293
Closed Access | Times Cited: 3
A deep learning approach for health monitoring in rotating machineries using vibrations and thermal features
Pauline Ong, Anelka John Koshy, Kee Huong Lai, et al.
Decision Analytics Journal (2024) Vol. 10, pp. 100399-100399
Open Access | Times Cited: 2
Motor PHM on Edge Computing with Anomaly Detection and Fault Severity Estimation through Compressed Data Using PCA and Autoencoder
Jong Hyun Choi, Sung Kyu Jang, Woon Hyung Cho, et al.
Machine Learning and Knowledge Extraction (2024) Vol. 6, Iss. 3, pp. 1466-1483
Open Access | Times Cited: 2
Support matrix machine: A review
Anuradha Kumari, Mushir Akhtar, Rupal Shah, et al.
Neural Networks (2024) Vol. 181, pp. 106767-106767
Open Access | Times Cited: 2
A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography
Omar Trejo-Chavez, Irving A. Cruz‐Albarran, Emmanuel Resendiz‐Ochoa, et al.
Machines (2023) Vol. 11, Iss. 7, pp. 752-752
Open Access | Times Cited: 6
A Semi-Supervised Adaptive Matrix Machine Approach for Fault Diagnosis in Railway Switch Machine
Wenqing Li, Zhongwei Xu, Meng Mei, et al.
Sensors (2024) Vol. 24, Iss. 13, pp. 4402-4402
Open Access | Times Cited: 1
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