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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.

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Showing 1-25 of 139 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: 520
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
Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network
Yiwei Cheng, Manxi Lin, Jun Wu, et al.
Knowledge-Based Systems (2021) Vol. 216, pp. 106796-106796
Closed Access | Times Cited: 245
Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016
Xiaohan Chen, Rui Yang, Yihao Xue, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-21
Open Access | Times Cited: 185
A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations
Mohammed Hakim, Abdoulhdi A. Borhana Omran, Ali Najah Ahmed, et al.
Ain Shams Engineering Journal (2022) Vol. 14, Iss. 4, pp. 101945-101945
Open Access | Times Cited: 129
Machinery Fault Diagnosis Based on Domain Adaptation to Bridge the Gap Between Simulation and Measured Signals
Yunxia Lou, Anil Kumar, Jiawei Xiang
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-9
Closed Access | Times Cited: 86
A survey of transfer learning for machinery diagnostics and prognostics
Siya Yao, Qi Kang, MengChu Zhou, et al.
Artificial Intelligence Review (2022) Vol. 56, Iss. 4, pp. 2871-2922
Closed Access | Times Cited: 74
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
An unsupervised domain adaptation approach with enhanced transferability and discriminability for bearing fault diagnosis under few-shot samples
Wengang Ma, Yadong Zhang, Liang Ma, et al.
Expert Systems with Applications (2023) Vol. 225, pp. 120084-120084
Closed Access | Times Cited: 44
A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0
Mehdi Saman Azari, Francesco Flammini, Stefania Santini, et al.
IEEE Access (2023) Vol. 11, pp. 12887-12910
Open Access | Times Cited: 42
Rolling bearing fault diagnosis using optimal ensemble deep transfer network
Xingqiu Li, Hongkai Jiang, Ruixin Wang, et al.
Knowledge-Based Systems (2020) Vol. 213, pp. 106695-106695
Closed Access | Times Cited: 134
Deep Adversarial Capsule Network for Compound Fault Diagnosis of Machinery Toward Multidomain Generalization Task
Ruyi Huang, Jipu Li, Yixiao Liao, et al.
IEEE Transactions on Instrumentation and Measurement (2020) Vol. 70, pp. 1-11
Closed Access | Times Cited: 123
Zero-shot learning for compound fault diagnosis of bearings
Juan Xu, Long Zhou, Weihua Zhao, et al.
Expert Systems with Applications (2021) Vol. 190, pp. 116197-116197
Closed Access | Times Cited: 93
Deep multi-scale adversarial network with attention: A novel domain adaptation method for intelligent fault diagnosis
Bo Zhao, Xianmin Zhang, Zhenhui Zhan, et al.
Journal of Manufacturing Systems (2021) Vol. 59, pp. 565-576
Closed Access | Times Cited: 80
Multiscale Convolutional Neural Network With Feature Alignment for Bearing Fault Diagnosis
Junbin Chen, Ruyi Huang, Kun Zhao, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-10
Closed Access | Times Cited: 68
A Multi-Input and Multi-Task Convolutional Neural Network for Fault Diagnosis Based on Bearing Vibration Signal
Yang Wang, Miaomiao Yang, Yong Li, et al.
IEEE Sensors Journal (2021) Vol. 21, Iss. 9, pp. 10946-10956
Closed Access | Times Cited: 67
Deep multiple auto-encoder with attention mechanism network: A dynamic domain adaptation method for rotary machine fault diagnosis under different working conditions
Shengkang Yang, Xianguang Kong, Qibin Wang, et al.
Knowledge-Based Systems (2022) Vol. 249, pp. 108639-108639
Closed Access | Times Cited: 63
Bearing fault diagnosis based on optimal convolution neural network
Yongjian Sun, Shaohui Li
Measurement (2022) Vol. 190, pp. 110702-110702
Closed Access | Times Cited: 57
A multi-source ensemble domain adaptation method for rotary machine fault diagnosis
Shengkang Yang, Xianguang Kong, Qibin Wang, et al.
Measurement (2021) Vol. 186, pp. 110213-110213
Closed Access | Times Cited: 56
Rolling Bearing Compound Fault Diagnosis Based on Parameter Optimization MCKD and Convolutional Neural Network
Shuzhi Gao, Shuo Shi, Yimin Zhang
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-8
Closed Access | Times Cited: 50
An intelligent fault diagnosis for machine maintenance using weighted soft-voting rule based multi-attention module with multi-scale information fusion
Zifei Xu, Musa Bashir, Wanfu Zhang, et al.
Information Fusion (2022) Vol. 86-87, pp. 17-29
Open Access | Times Cited: 48
A convolutional neural network method based on Adam optimizer with power-exponential learning rate for bearing fault diagnosis
Youming Wang, Zhao Xiao, Gongqing Cao
Journal of Vibroengineering (2022) Vol. 24, Iss. 4, pp. 666-678
Open Access | Times Cited: 46
Identification method for subgrade settlement of ballastless track based on vehicle vibration signals and machine learning
Juanjuan Ren, Wei Liu, Wei Du, et al.
Construction and Building Materials (2023) Vol. 369, pp. 130573-130573
Closed Access | Times Cited: 35
A federated transfer learning method with low-quality knowledge filtering and dynamic model aggregation for rolling bearing fault diagnosis
Ran Wang, Fucheng Yan, Liang Yu, et al.
Mechanical Systems and Signal Processing (2023) Vol. 198, pp. 110413-110413
Closed Access | Times Cited: 31
Mechanical fault diagnosis based on deep transfer learning: a review
Dalian Yang, Wen-Bin Zhang, Yong-Zheng Jiang
Measurement Science and Technology (2023) Vol. 34, Iss. 11, pp. 112001-112001
Closed Access | Times Cited: 31
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