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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:

DGTL-Net: A Deep Generative Transfer Learning Network for Fault Diagnostics on New Hard Disks
Chang Shi, Zhenyu Wu, Xiaomeng Lv, et al.
Expert Systems with Applications (2020) Vol. 169, pp. 114379-114379
Closed Access | Times Cited: 14

Showing 14 citing articles:

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
TinyML-enabled edge implementation of transfer learning framework for domain generalization in machine fault diagnosis
Supriya Asutkar, Chaitravi Chalke, Kajal Shivgan, et al.
Expert Systems with Applications (2022) Vol. 213, pp. 119016-119016
Closed Access | Times Cited: 41
Revolution and peak discrepancy-based domain alignment method for bearing fault diagnosis under very low-speed conditions
Seung-Yun Lee, Sungjong Kim, Su J. Kim, et al.
Expert Systems with Applications (2024) Vol. 251, pp. 124084-124084
Closed Access | Times Cited: 5
Industrial data-driven modeling for imbalanced fault diagnosis
Kuo‐Yi Lin, Thitipong Jamrus
Industrial Management & Data Systems (2024) Vol. 124, Iss. 11, pp. 3108-3137
Closed Access | Times Cited: 5
Fusing logic rule-based hybrid variable graph neural network approaches to fault diagnosis of industrial processes
Yin Min, Jince Li, Yilin Shi, et al.
Expert Systems with Applications (2023) Vol. 238, pp. 121753-121753
Closed Access | Times Cited: 13
A source free robust domain adaptation approach with pseudo-labels uncertainty estimation for rolling bearing fault diagnosis under limited sample conditions
Ruiqi Liu, Wengang Ma, Feipeng Kuang, et al.
Knowledge-Based Systems (2024) Vol. 304, pp. 112443-112443
Closed Access | Times Cited: 2
A Survey on Machine Learning based Smart Maintenance and Quality Control Solutions
Attila Frankó, Pál Varga
Híradástechnika/Infocommunications journal (2021) Vol. 13, Iss. 4, pp. 28-35
Open Access | Times Cited: 14
Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges
Marcel Braig, Peter Zeiler
IEEE Access (2022) Vol. 11, pp. 1506-1554
Open Access | Times Cited: 7
Hard Disk Failure Prediction on Highly Imbalanced Data using LSTM Network
Cahyadi Cahyadi, Matthew Forshaw
2021 IEEE International Conference on Big Data (Big Data) (2021), pp. 3985-3991
Closed Access | Times Cited: 8
Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators
Junjie Shi, Jing Du, Yingwen Ren, et al.
Journal of Sensors (2022) Vol. 2022, pp. 1-15
Open Access | Times Cited: 3
Типичные ошибки в больших данных по надежности накопителей информации в data-центрах
Искандар Наилович Насыров, Ильдар Искандарович Насыров, Рустам Искандарович Насыров
Экономика Информатика (2024) Vol. 51, Iss. 2, pp. 479-488
Open Access
A Semi-supervised Deep Learning Model with Consistency Regularization of Augmented Samples for Imbalanced Fault Detection
Hao Chen, Junhua Han, Xiaomeng Lv, et al.
(2022), pp. 290-295
Closed Access | Times Cited: 2
Genetic Algorithm for feature selection in enhancing the hard disk failure prediction using artificial neural network
M. Sobhana, Gajula Siva Sai Preethi, Kancharla Bhanu Sujitha, et al.
(2022), pp. 1-7
Closed Access | Times Cited: 1
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