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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 transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions
Han Cheng, Xianguang Kong, Qibin Wang, et al.
Journal of Intelligent Manufacturing (2021) Vol. 34, Iss. 2, pp. 587-613
Closed Access | Times Cited: 47

Showing 26-50 of 47 citing articles:

Machining quality prediction of multi-feature parts using integrated multi-source domain dynamic adaptive transfer learning
Pei Wang, Jingshuai Qi, Xun Xu, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 90, pp. 102815-102815
Open Access | Times Cited: 2
HG-XAI: human-guided tool wear identification approach through augmentation of explainable artificial intelligence with machine vision
Aitha Sudheer Kumar, Ankit Agarwal, Vinita Gangaram Jansari, et al.
Journal of Intelligent Manufacturing (2024)
Closed Access | Times Cited: 2
A Study of a Domain-Adaptive LSTM-DNN-Based Method for Remaining Useful Life Prediction of Planetary Gearbox
Zixuan Liu, Chaobin Tan, Yuxin Liu, et al.
Processes (2023) Vol. 11, Iss. 7, pp. 2002-2002
Open Access | Times Cited: 5
A mechanical assembly monitoring method based on domain adaptive semantic segmentation
Jinlei Wang, Chengjun Chen, Chenggang Dai
The International Journal of Advanced Manufacturing Technology (2023) Vol. 128, Iss. 1-2, pp. 625-637
Closed Access | Times Cited: 5
Multi-representation transferable attention network for remaining useful life prediction of rolling bearings under multiple working conditions
Yabin Shi, Youchang Cui, Han Cheng, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 2, pp. 025037-025037
Closed Access | Times Cited: 5
Enhancing aircraft engine remaining useful life prediction via multiscale deep transfer learning with limited data
Qi Liu, Zhiyao Zhang, Peng Guo, et al.
Journal of Computational Design and Engineering (2023) Vol. 11, Iss. 1, pp. 343-355
Open Access | Times Cited: 4
Label Adversarial Domain Adaptation Network for Predicting Remaining Useful Life Based on Cross-Domain Condition
Shanshan Lv, Chengcheng Xia, Cong Cheng, et al.
(2024)
Closed Access | Times Cited: 1
Label adversarial domain adaptation network for predicting remaining useful life based on cross-domain condition
Shanshan Lv, Chengcheng Xia, Cong Cheng, et al.
Computers & Industrial Engineering (2024) Vol. 197, pp. 110542-110542
Closed Access | Times Cited: 1
Novel tool wear prediction method based on multimodal information fusion and deep subdomain adaptation
Wen Hou, Jiachang Wang, Leilei Wang, et al.
Mechanical Systems and Signal Processing (2024) Vol. 224, pp. 112128-112128
Closed Access | Times Cited: 1
Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN
Ning Hu, Zhenguo Liu, Shixin Jiang, et al.
Shock and Vibration (2023) Vol. 2023, pp. 1-14
Open Access | Times Cited: 3
Remaining useful life prediction of circuit breaker operating mechanisms based on wavelet-enhanced dual-tree residual networks
Tailong Wu, Yuan Yao, Zhihao Li, et al.
Journal of Power Electronics (2023) Vol. 24, Iss. 1, pp. 78-91
Closed Access | Times Cited: 3
A novel transfer learning approach based on deep degradation feature adaptive alignment for remaining useful life prediction with multi-condition data
Yi Lyu, Zhenfei Wen, Aiguo Chen
Journal of Intelligent Manufacturing (2023)
Closed Access | Times Cited: 2
Black Box Adversarial Reprogramming for Time Series Feature Classification in Ball Bearings’ Remaining Useful Life Classification
Alexander Bott, Felix Schreyer, Alexander Puchta, et al.
Machine Learning and Knowledge Extraction (2024) Vol. 6, Iss. 3, pp. 1969-1996
Open Access
Multivariate quality prediction of thin-walled parts machining using multi-task parallel deep transfer learning
Pei Wang, Pengde Huang, Haizhen Tao, et al.
International Journal of Production Research (2024), pp. 1-32
Closed Access
Deep transfer learning in machinery remaining useful life prediction: A systematic review
Gaige Chen, Xianguang Kong, Han Cheng, et al.
Measurement Science and Technology (2024) Vol. 36, Iss. 1, pp. 012005-012005
Closed Access
Remaining Useful Life Prediction of Main Reducer Based on Parallel Multi-Attention and Contrastive Fusion Multi-Source Domain Adaption Considering Compound-Fault
Qing Ye, Muyu Lin, Hai-Meng Zhou, et al.
(2024)
Closed Access
Feature learning for bearing prognostics: A comprehensive review of machine/deep learning methods, challenges, and opportunities
Ahmed Ayman, Ahmed Onsy, Omneya Attallah, et al.
Measurement (2024), pp. 116589-116589
Open Access
A vision subsampling probsparse self-attention-based informer for bearing remaining useful life prediction
MingLei Li, Yanfeng Geng, Guangliang Pan, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 9, pp. 095107-095107
Closed Access | Times Cited: 1
IDIDNG: A Domain Generalization Remaining Useful Life Prediction Method of Unknown Bearings
Juan Xu, Xu Zhen
(2022), pp. 1-6
Closed Access | Times Cited: 1
ATPRINPM: A single-source domain generalization method for the remaining useful life prediction of unknown bearings
Juan Xu, Bin Ma, Yuqi Fan, et al.
(2022), pp. 1-6
Closed Access | Times Cited: 1
A Soft Sensor Method based on Unsupervised Multi-layer Domain Adaptation for Batch Processes
Qin Xiong, Huaiping Jin, Bin Wang, et al.
2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS) (2023) Vol. 30, pp. 1805-1811
Closed Access
A Novel Deep Multi-Source Domain Adaptation Framework for Remaining Useful Life Prediction Across Operating Condition
Junrong Du, Xuanang Gui, Lei Song, et al.
(2023), pp. 1-5
Closed Access
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