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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:

Modified DSAN for unsupervised cross-domain fault diagnosis of bearing under speed fluctuation
Jingjie Luo, Haidong Shao, Hongru Cao, et al.
Journal of Manufacturing Systems (2022) Vol. 65, pp. 180-191
Open Access | Times Cited: 45

Showing 26-50 of 45 citing articles:

Bi-level binary coded fully connected classifier based on residual network 50 with bottom and deep level features for bearing fault diagnosis
Linfei Yin, Zixuan Wang
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108342-108342
Closed Access | Times Cited: 2
Feature structured domain adaptation for quality prediction of cross working conditions in industrial processes
He Ding, Kuangrong Hao, Lei Chen, et al.
Journal of Manufacturing Systems (2024) Vol. 74, pp. 887-900
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
Transfer learning for bearing fault diagnosis: adaptive batch normalization and combined optimization method
Xueyi Li, Kaiyu Su, Daiyou Li, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 4, pp. 046106-046106
Closed Access | Times Cited: 4
A New Dual-domain Signal Collaborative Transfer Network for Bearing Fault Diagnosis
Shuo Xing, Jinrui Wang, Junqing Ma, et al.
IEEE Transactions on Instrumentation and Measurement (2024) Vol. 73, pp. 1-11
Closed Access | Times Cited: 1
A hybrid dynamic adversarial domain adaptation network with multi-channel attention mechanism for rotating machinery unsupervised fault diagnosis under varying operating conditions
Hangbo Duan, Zongyan Cai, Yuanbo Xu
Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science (2024) Vol. 238, Iss. 21, pp. 10566-10577
Closed Access | Times Cited: 1
Simulation data-driven attention fusion network with multi-similarity metric: A single-domain generalization diagnostic method for tie rod bolt loosening of a rod-fastening rotor system
Chen Zhou, Jun He, Xin Xiong, et al.
Measurement (2024) Vol. 240, pp. 115565-115565
Closed Access | Times Cited: 1
A multi-source unsupervised fault diagnosis network with residual enhancement attention module for rotating machinery cross-operating conditions
Hangbo Duan, Zongyan Cai, Qingtao Liu, et al.
Journal of Vibration and Control (2024)
Closed Access | Times Cited: 1
Utilizing Bayesian generalization network for reliable fault diagnosis of machinery with limited data
Minjie Feng, Haidong Shao, Minghui Shao, et al.
Knowledge-Based Systems (2024) Vol. 305, pp. 112628-112628
Closed Access | Times Cited: 1
A three-stage bearing transfer fault diagnosis method for large domain shift scenarios
Kai Huang, Zhijun Ren, Linbo Zhu, et al.
Reliability Engineering & System Safety (2024) Vol. 254, pp. 110641-110641
Closed Access | Times Cited: 1
A Hybrid Adversarial Domain Adaptation Network for Bearing Fault Diagnosis Under Varying Working Conditions
Ziyun Zhang, Lei Peng, Guangming Dai, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-13
Closed Access | Times Cited: 4
A novel collaborative bearing fault diagnosis method based on multi-scale dynamic fusion network under speed fluctuating condition
Shuo Xing, Jinrui Wang, Baokun Han, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 1, pp. 015126-015126
Closed Access | Times Cited: 3
A renewed adversarial network for bearing fault diagnosis based on vibro-acoustic signals under speed fluctuating conditions
Shuo Xing, Jinrui Wang, Baokun Han, et al.
Journal of Vibration and Control (2023) Vol. 30, Iss. 19-20, pp. 4612-4623
Open Access | Times Cited: 2
A Multisource Scale Subdomain Adaptation Network With Multihead Hybrid Self-Attention for Remaining Useful Life Prediction of Rolling Bearings
Hao Tian, Yan Han, Pingan Yang, et al.
IEEE Sensors Journal (2023) Vol. 24, Iss. 3, pp. 3098-3112
Closed Access | Times Cited: 2
DPICEN: Deep Physical Information Consistency Embedded Network for Bearing Fault Diagnosis under Unknown Domain
Feiyu Lu, Qingbin Tong, Xuedong Jiang, et al.
Reliability Engineering & System Safety (2024) Vol. 252, pp. 110454-110454
Closed Access
A simulated-to-real transfer fault diagnosis method based on prototype clustering subdomain adversarial adaptation network for HST bogie bearing
Jingsong Xie, Shangjun Wang, Yuyan Li, et al.
IEEE Transactions on Instrumentation and Measurement (2024) Vol. 73, pp. 1-13
Closed Access
Bearing Fault Diagnosis Method Based on Multi-axis Vision Transformer and Weighted Subdomain Adaptive Adversarial Network
Zehuan Li, Shunsheng Guo, Li Jiang
Lecture notes in electrical engineering (2024), pp. 496-502
Closed Access
Fault diagnosis of rolling bearings driven by multi-channel data fusion and feature fusion under time-varying speed conditions
Zonghao Jiao, Zhongwei Zhang, Y F Li, et al.
Measurement Science and Technology (2024) Vol. 36, Iss. 1, pp. 015125-015125
Closed Access
A graph representation learning-based method for fault diagnosis of rotating machinery under time-varying speed conditions
Sichao Sun, Xinyu Xia, Zhou Hua
Research Square (Research Square) (2024)
Open Access
Dual-domain Signal Fusion Adversarial Network Based Vision Transformer for Cross-domain Fault Diagnosis
Shuo Xing, Jinrui Wang, Baokun Han, et al.
(2023), pp. 1-5
Closed Access | Times Cited: 1
A Novel Transformer Model Based on Dynamic Convolution and ProbSparse Self-Attention for RUL Prediction of Bearings
Yancheng Zhu, Hao Su, Ling Xiang, et al.
(2023), pp. 1-6
Closed Access | Times Cited: 1
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