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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:

Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places
Xiang Li, Zhang We, Nan-Xi Xu, et al.
IEEE Transactions on Industrial Electronics (2019) Vol. 67, Iss. 8, pp. 6785-6794
Closed Access | Times Cited: 181

Showing 1-25 of 181 citing articles:

A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
Weihua Li, Ruyi Huang, Jipu Li, et al.
Mechanical Systems and Signal Processing (2021) Vol. 167, pp. 108487-108487
Open Access | Times Cited: 523
Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review
Dhiraj Neupane, Jongwon Seok
IEEE Access (2020) Vol. 8, pp. 93155-93178
Open Access | Times Cited: 373
Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study
Zhibin Zhao, Qiyang Zhang, Xiaolei Yu, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-28
Open Access | Times Cited: 325
Machinery fault diagnosis with imbalanced data using deep generative adversarial networks
Zhang We, Xiang Li, Xiaodong Jia, et al.
Measurement (2019) Vol. 152, pp. 107377-107377
Closed Access | Times Cited: 299
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: 283
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
Behnoush Rezaeianjouybari, Yi Shang
Measurement (2020) Vol. 163, pp. 107929-107929
Closed Access | Times Cited: 231
Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework
Wei Li, Xiang Zhong, Haidong Shao, et al.
Advanced Engineering Informatics (2022) Vol. 52, pp. 101552-101552
Closed Access | Times Cited: 216
Open-Set Domain Adaptation in Machinery Fault Diagnostics Using Instance-Level Weighted Adversarial Learning
Zhang We, Xiang Li, Hui Ma, et al.
IEEE Transactions on Industrial Informatics (2021) Vol. 17, Iss. 11, pp. 7445-7455
Closed Access | Times Cited: 185
Deep transfer learning with limited data for machinery fault diagnosis
Te Han, Chao Liu, Rui Wu, et al.
Applied Soft Computing (2021) Vol. 103, pp. 107150-107150
Closed Access | Times Cited: 170
Deep Learning-Based Partial Domain Adaptation Method on Intelligent Machinery Fault Diagnostics
Xiang Li, Zhang We
IEEE Transactions on Industrial Electronics (2020) Vol. 68, Iss. 5, pp. 4351-4361
Closed Access | Times Cited: 169
Deep learning-based prognostic approach for lithium-ion batteries with adaptive time-series prediction and on-line validation
Zhang We, Xiang Li, Xu Li
Measurement (2020) Vol. 164, pp. 108052-108052
Closed Access | Times Cited: 162
A Novel Weighted Adversarial Transfer Network for Partial Domain Fault Diagnosis of Machinery
Weihua Li, Zhuyun Chen, Guolin He
IEEE Transactions on Industrial Informatics (2020) Vol. 17, Iss. 3, pp. 1753-1762
Closed Access | Times Cited: 160
Metric-based meta-learning model for few-shot fault diagnosis under multiple limited data conditions
Duo Wang, Ming Zhang, Yuchun Xu, et al.
Mechanical Systems and Signal Processing (2021) Vol. 155, pp. 107510-107510
Open Access | Times Cited: 154
Data alignments in machinery remaining useful life prediction using deep adversarial neural networks
Xiang Li, Zhang We, Hui Ma, et al.
Knowledge-Based Systems (2020) Vol. 197, pp. 105843-105843
Closed Access | Times Cited: 147
A novel deep convolution multi-adversarial domain adaptation model for rolling bearing fault diagnosis
Lanjun Wan, Yuanyuan Li, Keyu Chen, et al.
Measurement (2022) Vol. 191, pp. 110752-110752
Closed Access | Times Cited: 140
Deep multi-scale convolutional transfer learning network: A novel method for intelligent fault diagnosis of rolling bearings under variable working conditions and domains
Bo Zhao, Xianmin Zhang, Zhenhui Zhan, et al.
Neurocomputing (2020) Vol. 407, pp. 24-38
Closed Access | Times Cited: 139
Entropy Measures in Machine Fault Diagnosis: Insights and Applications
Zhiqiang Huo, Miguel Martínez-García, Yu Zhang, et al.
IEEE Transactions on Instrumentation and Measurement (2020) Vol. 69, Iss. 6, pp. 2607-2620
Open Access | Times Cited: 138
Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery
Xiang Li, Xu Li, Hui Ma
Mechanical Systems and Signal Processing (2020) Vol. 143, pp. 106825-106825
Closed Access | Times Cited: 137
Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions
Zhang We, Xiang Li, Hui Ma, et al.
Reliability Engineering & System Safety (2021) Vol. 211, pp. 107556-107556
Closed Access | Times Cited: 130
Universal source-free domain adaptation method for cross-domain fault diagnosis of machines
Yongchao Zhang, Zhaohui Ren, Ke Feng, et al.
Mechanical Systems and Signal Processing (2023) Vol. 191, pp. 110159-110159
Closed Access | Times Cited: 90
Supervised Contrastive Learning-Based Domain Adaptation Network for Intelligent Unsupervised Fault Diagnosis of Rolling Bearing
Yongchao Zhang, Zhaohui Ren, Shihua Zhou, et al.
IEEE/ASME Transactions on Mechatronics (2022) Vol. 27, Iss. 6, pp. 5371-5380
Closed Access | Times Cited: 76
A domain generalization network combing invariance and specificity towards real-time intelligent fault diagnosis
Chao Zhao, Weiming Shen
Mechanical Systems and Signal Processing (2022) Vol. 173, pp. 108990-108990
Closed Access | Times Cited: 71
Adversarial Mutual Information-Guided Single Domain Generalization Network for Intelligent Fault Diagnosis
Chao Zhao, Weiming Shen
IEEE Transactions on Industrial Informatics (2022) Vol. 19, Iss. 3, pp. 2909-2918
Closed Access | Times Cited: 71
Fault diagnosis in rotating machines based on transfer learning: Literature review
Iqbal Misbah, C.K.M. Lee, K. L. Keung
Knowledge-Based Systems (2023) Vol. 283, pp. 111158-111158
Closed Access | Times Cited: 57
Deep Mixed Domain Generalization Network for Intelligent Fault Diagnosis Under Unseen Conditions
Zhenhua Fan, Qifa Xu, Cuixia Jiang, et al.
IEEE Transactions on Industrial Electronics (2023) Vol. 71, Iss. 1, pp. 965-974
Closed Access | Times Cited: 51
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