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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:

A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Zhiqin Zhu, Yangbo Lei, Guanqiu Qi, et al.
Measurement (2022) Vol. 206, pp. 112346-112346
Closed Access | Times Cited: 281

Showing 1-25 of 281 citing articles:

A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication
Dongdong Liu, Lingli Cui, Weidong Cheng
Measurement Science and Technology (2023) Vol. 35, Iss. 1, pp. 012002-012002
Closed Access | Times Cited: 80
Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review
Dongdong Liu, Lingli Cui, Huaqing Wang
IEEE Sensors Journal (2023) Vol. 23, Iss. 24, pp. 29969-29990
Open Access | Times Cited: 53
A multi-source domain information fusion network for rotating machinery fault diagnosis under variable operating conditions
Tianyu Gao, Jingli Yang, Qing Tang
Information Fusion (2024) Vol. 106, pp. 102278-102278
Closed Access | Times Cited: 46
A self-attention based contrastive learning method for bearing fault diagnosis
Long Cui, Xincheng Tian, Qingzhe Wei, et al.
Expert Systems with Applications (2023) Vol. 238, pp. 121645-121645
Closed Access | Times Cited: 39
A light deep adaptive framework toward fault diagnosis of a hydraulic piston pump
Shengnan Tang, Boo Cheong Khoo, Yong Zhu, et al.
Applied Acoustics (2024) Vol. 217, pp. 109807-109807
Closed Access | Times Cited: 26
Insights into modern machine learning approaches for bearing fault classification: A systematic literature review
Afzal Ahmed Soomro, Masdi Muhammad, Ainul Akmar Mokhtar, et al.
Results in Engineering (2024) Vol. 23, pp. 102700-102700
Open Access | Times Cited: 18
Application of deep learning to fault diagnosis of rotating machineries
Hao Su, Ling Xiang, Aijun Hu
Measurement Science and Technology (2024) Vol. 35, Iss. 4, pp. 042003-042003
Open Access | Times Cited: 15
Single-domain incremental generation network for machinery intelligent fault diagnosis under unknown working speeds
Yuanyue Pu, Jian Tang, Xue‐Gang Li, et al.
Advanced Engineering Informatics (2024) Vol. 60, pp. 102400-102400
Closed Access | Times Cited: 15
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 new bearing fault diagnosis method via simulation data driving transfer learning without target fault data
Wenbo Hou, Chunlin Zhang, Yunqian Jiang, et al.
Measurement (2023) Vol. 215, pp. 112879-112879
Closed Access | Times Cited: 30
Gaussian Mixture Variational-Based Transformer Domain Adaptation Fault Diagnosis Method and Its Application in Bearing Fault Diagnosis
Yiyao An, Ke Zhang, Yi Chai, et al.
IEEE Transactions on Industrial Informatics (2023) Vol. 20, Iss. 1, pp. 615-625
Closed Access | Times Cited: 29
Machine learning for fault analysis in rotating machinery: A comprehensive review
Oğuzhan Daş, Duygu Bağcı Daş, Derya Birant
Heliyon (2023) Vol. 9, Iss. 6, pp. e17584-e17584
Open Access | Times Cited: 29
CBAM-CRLSGAN: A novel fault diagnosis method for planetary transmission systems under small samples scenarios
Jie Zhang, Yun Kong, Zhuyun Chen, et al.
Measurement (2024) Vol. 234, pp. 114795-114795
Closed Access | Times Cited: 13
Novel cloud-AIoT fault diagnosis for industrial diesel generators based hybrid deep learning CNN-BGRU algorithm
Thao Nguyen Da, Phuong Nguyen Thanh, Ming-Yuan Cho
Internet of Things (2024) Vol. 26, pp. 101164-101164
Closed Access | Times Cited: 11
Deep adaptive sparse residual networks: A lifelong learning framework for rotating machinery fault diagnosis with domain increments
Yan Zhang, Changqing Shen, Juanjuan Shi, et al.
Knowledge-Based Systems (2024) Vol. 293, pp. 111679-111679
Closed Access | Times Cited: 10
A novel self-supervised representation learning framework based on time-frequency alignment and interaction for mechanical fault diagnosis
Daxing Fu, Jie Liu, Hao Zhong, et al.
Knowledge-Based Systems (2024) Vol. 295, pp. 111846-111846
Closed Access | Times Cited: 10
A review on convolutional neural network in rolling bearing fault diagnosis
Xin Li, Zengqiang Ma, Zonghao Yuan, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 7, pp. 072002-072002
Closed Access | Times Cited: 9
A multi-scale graph convolutional network with contrastive-learning enhanced self-attention pooling for intelligent fault diagnosis of gearbox
Zixu Chen, Jinchen Ji, Wennian Yu, et al.
Measurement (2024) Vol. 230, pp. 114497-114497
Closed Access | Times Cited: 8
Gearbox fault diagnosis method based on lightweight channel attention mechanism and transfer learning
Xuemin Cheng, Shuihai Dou, Yanping Du, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Decoupled interpretable robust domain generalization networks: A fault diagnosis approach across bearings, working conditions, and artificial-to-real scenarios
Qiu‐Ning Zhu, Hongqi Liu, Chenyu Bao, et al.
Advanced Engineering Informatics (2024) Vol. 61, pp. 102445-102445
Closed Access | Times Cited: 7
A rolling bearing fault diagnosis method based on interactive generative feature space oversampling-based autoencoder under imbalanced data
F Huang, Kai Zhang, Zhixuan Li, et al.
Structural Health Monitoring (2024)
Closed Access | Times Cited: 7
Real-time AIoT anomaly detection for industrial diesel generator based an efficient deep learning CNN-LSTM in industry 4.0
Thao Nguyen-Da, Phuong Nguyen Thanh, Ming-Yuan Cho
Internet of Things (2024) Vol. 27, pp. 101280-101280
Closed Access | Times Cited: 7
Fault diagnosis based on federated learning driven by dynamic expansion for model layers of imbalanced client
Funa Zhou, Shun Liu, Hamido Fujita, et al.
Expert Systems with Applications (2023) Vol. 238, pp. 121982-121982
Closed Access | Times Cited: 20
Data‐driven fault diagnosis approaches for industrial equipment: A review
Atma Sahu, Sanjay Kumar Palei, Aishwarya Mishra
Expert Systems (2023) Vol. 41, Iss. 2
Closed Access | Times Cited: 19
CFI-LFENet: Infusing cross-domain fusion image and lightweight feature enhanced network for fault diagnosis
Chao Lian, Yuliang Zhao, Jinliang Shao, et al.
Information Fusion (2023) Vol. 104, pp. 102162-102162
Closed Access | Times Cited: 19
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