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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:

A multi-ensemble method based on deep auto-encoders for fault diagnosis of rolling bearings
Xianguang Kong, Gang Mao, Qibin Wang, et al.
Measurement (2019) Vol. 151, pp. 107132-107132
Closed Access | Times Cited: 79

Showing 1-25 of 79 citing articles:

Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
Zhibin Zhao, Tianfu Li, Jingyao Wu, et al.
ISA Transactions (2020) Vol. 107, pp. 224-255
Open Access | Times Cited: 428
Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions
Bo Zhao, Xianmin Zhang, Hai Li, et al.
Knowledge-Based Systems (2020) Vol. 199, pp. 105971-105971
Closed Access | Times Cited: 302
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
A hybrid deep-learning model for fault diagnosis of rolling bearings
Xu Yang, Zhixiong Li, Shuqing Wang, et al.
Measurement (2020) Vol. 169, pp. 108502-108502
Closed Access | Times Cited: 224
A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions
Hao Su, Ling Xiang, Aijun Hu, et al.
Mechanical Systems and Signal Processing (2022) Vol. 169, pp. 108765-108765
Closed Access | Times Cited: 165
Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review
Zheng Rong Yang, Binbin Xu, Wei Luo, et al.
Measurement (2021) Vol. 189, pp. 110460-110460
Closed Access | Times Cited: 139
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: 134
A Review of Data-Driven Machinery Fault Diagnosis Using Machine Learning Algorithms
Jian Cen, Zhuohong Yang, Xi Liu, et al.
Journal of Vibration Engineering & Technologies (2022) Vol. 10, Iss. 7, pp. 2481-2507
Closed Access | Times Cited: 88
Insights into modern machine learning approaches for bearing fault classification: A systematic literature review
Afzal Ahmed Soomro, Masdi Muhammad, Ainul Akmar Mokhtar, et al.
Results in Engineering (2024) Vol. 23, pp. 102700-102700
Open Access | Times Cited: 18
Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation
Ruxue Bai, Quansheng Xu, Zong Meng, et al.
Measurement (2021) Vol. 184, pp. 109885-109885
Closed Access | Times Cited: 97
Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion
Huibin Zhu, Zhangming He, Juhui Wei, et al.
Sensors (2021) Vol. 21, Iss. 7, pp. 2524-2524
Open Access | Times Cited: 68
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
Fractional Fourier and time domain recurrence plot fusion combining convolutional neural network for bearing fault diagnosis under variable working conditions
Ruxue Bai, Zong Meng, Quansheng Xu, et al.
Reliability Engineering & System Safety (2022) Vol. 232, pp. 109076-109076
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
Interactive dual adversarial neural network framework: An open-set domain adaptation intelligent fault diagnosis method of rotating machinery
Gang Mao, Yongbo Li, Sixiang Jia, et al.
Measurement (2022) Vol. 195, pp. 111125-111125
Closed Access | Times Cited: 44
A domain adaptation method for bearing fault diagnosis using multiple incomplete source data
Qibin Wang, Yuanbing Xu, Shengkang Yang, et al.
Journal of Intelligent Manufacturing (2023) Vol. 35, Iss. 2, pp. 777-791
Closed Access | Times Cited: 27
A collaborative central domain adaptation approach with multi-order graph embedding for bearing fault diagnosis under few-shot samples
Wengang Ma, Ruiqi Liu, Jin Guo, et al.
Applied Soft Computing (2023) Vol. 140, pp. 110243-110243
Closed Access | Times Cited: 22
A machine-learning based data-oriented pipeline for Prognosis and Health Management Systems
Marcos Leandro Hoffmann Souza, Cristiano André da Costa, Gabriel de Oliveira Ramos
Computers in Industry (2023) Vol. 148, pp. 103903-103903
Closed Access | Times Cited: 22
A broad learning model guided by global and local receptive causal features for online incremental machinery fault diagnosis
Xuefang Xu, Shuo Bao, Pengfei Liang, et al.
Expert Systems with Applications (2024) Vol. 246, pp. 123124-123124
Closed Access | Times Cited: 10
Improved weighted extreme learning machine with adaptive cost-sensitive strategy for imbalanced fault diagnosis of rotating machinery
Yinghao Zhao, Xu Yang, Jian Huang, et al.
Mechanical Systems and Signal Processing (2024) Vol. 217, pp. 111526-111526
Closed Access | Times Cited: 8
A consistency regularization based semi-supervised learning approach for intelligent fault diagnosis of rolling bearing
Kun Yu, Hui Ma, Tian Ran Lin, et al.
Measurement (2020) Vol. 165, pp. 107987-107987
Closed Access | Times Cited: 64
Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning
Farzin Piltan, Jong-Myon Kim
Applied Sciences (2021) Vol. 11, Iss. 10, pp. 4602-4602
Open Access | Times Cited: 47
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
Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions
Gang Mao, Zhongzheng Zhang, Bin Qiao, et al.
Entropy (2022) Vol. 24, Iss. 1, pp. 119-119
Open Access | Times Cited: 36
A novel unsupervised directed hierarchical graph network with clustering representation for intelligent fault diagnosis of machines
Bo Zhao, Xianmin Zhang, Qiqiang Wu, et al.
Mechanical Systems and Signal Processing (2022) Vol. 183, pp. 109615-109615
Closed Access | Times Cited: 32
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