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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:

Self-supervised bi-classifier adversarial transfer network for cross-domain fault diagnosis of rotating machinery
Jiachen Kuang, Guanghua Xu, Tangfei Tao, et al.
ISA Transactions (2022) Vol. 130, pp. 433-448
Closed Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

Fault transfer diagnosis of rolling bearings across multiple working conditions via subdomain adaptation and improved vision transformer network
Pengfei Liang, Zhuoze Yu, Bin Wang, et al.
Advanced Engineering Informatics (2023) Vol. 57, pp. 102075-102075
Closed Access | Times Cited: 70
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
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
A systematic literature review of deep learning for vibration-based fault diagnosis of critical rotating machinery: Limitations and challenges
Omri Matania, Itai Dattner, Jacob Bortman, et al.
Journal of Sound and Vibration (2024) Vol. 590, pp. 118562-118562
Closed Access | Times Cited: 15
Domain Conditioned Joint Adaptation Network for Intelligent Bearing Fault Diagnosis Across Different Positions and Machines
Jiachen Kuang, Guanghua Xu, Tangfei Tao, et al.
IEEE Sensors Journal (2023) Vol. 23, Iss. 4, pp. 4000-4010
Closed Access | Times Cited: 27
A self-supervised contrastive learning framework with the nearest neighbors matching for the fault diagnosis of marine machinery
Ruihan Wang, Hui Chen, Cong Guan
Ocean Engineering (2023) Vol. 270, pp. 113437-113437
Closed Access | Times Cited: 21
A novel causal feature learning-based domain generalization framework for bearing fault diagnosis with a mixture of data from multiple working conditions and machines
Liu Cheng, Xiangwei Kong, Yongchao Zhang, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102622-102622
Closed Access | Times Cited: 5
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
Self-supervised Progressive Learning for Fault Diagnosis under Limited Labeled Data and Varying Conditions
Qiuyu Song, Lidong Yang, Xingxing Jiang, et al.
Neurocomputing (2025), pp. 130126-130126
Closed Access
Dual-Weight Consistency-Induced Partial Domain Adaptation Network for Intelligent Fault Diagnosis of Machinery
Jiachen Kuang, Guanghua Xu, Tangfei Tao, et al.
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-12
Closed Access | Times Cited: 19
Balance-blended adversarial distribution and smooth-suppressed labels refinement network for partial transfer fault diagnosis
Fuzheng Liu, H Wang, Mingshun Jiang, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 135, pp. 108723-108723
Closed Access | Times Cited: 3
Fusing multichannel autoencoders with dynamic global loss for self-supervised fault diagnosis
Chuan Li, Manjun Xiong, Hongmeng Shen, et al.
Computers in Industry (2024) Vol. 164, pp. 104165-104165
Closed Access | Times Cited: 3
S3M: Two-Stage-Based Semi-Self-Supervised Method for Intelligent Bearing Fault Diagnosis
Liu Cheng, Rengen Wang, Haochen Qi, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-15
Closed Access | Times Cited: 9
Adversarial Deep Transfer Learning in Fault Diagnosis: Progress, Challenges, and Future Prospects
Yu Guo, Jundong Zhang, Bin Sun, et al.
Sensors (2023) Vol. 23, Iss. 16, pp. 7263-7263
Open Access | Times Cited: 9
Research on a small sample feature transfer method for fault diagnosis of reciprocating compressors
Yang Tang, Xiao Xiao, Xin Yang, et al.
Journal of Loss Prevention in the Process Industries (2023) Vol. 85, pp. 105163-105163
Closed Access | Times Cited: 7
Bi-Generator Cooperative Domain Adversarial Neural Network for Bearing Fault Diagnosis
Jingde Li, Changqing Shen, Juanjuan Shi, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 7, pp. 10584-10593
Closed Access | Times Cited: 2
Few-shot condition diagnosis of rolling bearing using adversarial transfer network with class aggregation-guided
Shaoning Tian, Dong Zhen, Guohua Sun, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 6, pp. 066120-066120
Closed Access | Times Cited: 2
A bearing fault diagnosis method for unknown operating conditions based on differentiated feature extraction
Wei Cao, Zong Meng, Jimeng Li, et al.
ISA Transactions (2024)
Closed Access | Times Cited: 2
Source-Free Cluster Adaptation for Privacy-Preserving Machinery Fault Diagnosis
Mengliang Zhu, Xiangyu Zeng, Jie Liu, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-10
Closed Access | Times Cited: 6
Multi-adversarial deep transfer network for multi-source open-set fault diagnosis of rotating machinery with category shift
Zuqiang Su, Weilong Jiang, Kai Chen, et al.
Knowledge-Based Systems (2023) Vol. 282, pp. 111106-111106
Closed Access | Times Cited: 5
Prototype-guided bi-level adversarial domain adaptation network for intelligent fault diagnosis of rotating machinery under various working conditions
Jiachen Kuang, Guanghua Xu, Sicong Zhang, et al.
Measurement Science and Technology (2022) Vol. 33, Iss. 11, pp. 115014-115014
Closed Access | Times Cited: 8
An unsupervised transfer learning approach for rolling bearing fault diagnosis based on dual pseudo-label screening
Chunran Huo, Weiyang Xu, Quan Jiang, et al.
Structural Health Monitoring (2023) Vol. 23, Iss. 4, pp. 2288-2309
Closed Access | Times Cited: 4
Pseudo-label guided dual classifier domain adversarial network for unsupervised cross-domain fault diagnosis with small samples
Yawei Sun, Hongfeng Tao, Vladimir Stojanović
Advanced Engineering Informatics (2024) Vol. 64, pp. 102986-102986
Closed Access | Times Cited: 1
A New Probability Guided Domain Adversarial Network for Bearing Fault Diagnosis
Jingde Li, Bojian Chen, Changqing Shen, et al.
IEEE Sensors Journal (2022) Vol. 23, Iss. 2, pp. 1462-1470
Closed Access | Times Cited: 7
A cross-domain intelligent fault diagnosis method based on multi-source domain feature adaptation and selection
Ning Jia, Weiguo Huang, Yao Cheng, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 4, pp. 046108-046108
Closed Access | Times Cited: 3
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