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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:

Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention
Tengda Huang, Sheng Fu, Haonan Feng, et al.
Energies (2019) Vol. 12, Iss. 20, pp. 3937-3937
Open Access | Times Cited: 37

Showing 1-25 of 37 citing articles:

Attention mechanism in intelligent fault diagnosis of machinery: A review of technique and application
Haixin Lv, Jinglong Chen, Tongyang Pan, et al.
Measurement (2022) Vol. 199, pp. 111594-111594
Closed Access | Times Cited: 148
A Decade Review on Smart Cities: Paradigms, Challenges and Opportunities
Tarana Singh, Arun Solanki, Sanjay Kumar Sharma, et al.
IEEE Access (2022) Vol. 10, pp. 68319-68364
Open Access | Times Cited: 100
Selective kernel convolution deep residual network based on channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis
Shuo Zhang, Zhiwen Liu, Yunping Chen, et al.
ISA Transactions (2022) Vol. 133, pp. 369-383
Closed Access | Times Cited: 84
Interpreting network knowledge with attention mechanism for bearing fault diagnosis
Zhibo Yang, Junpeng Zhang, Zhibin Zhao, et al.
Applied Soft Computing (2020) Vol. 97, pp. 106829-106829
Closed Access | Times Cited: 121
Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review
Zhibin Zhao, Jingyao Wu, Tianfu Li, et al.
Chinese Journal of Mechanical Engineering (2021) Vol. 34, Iss. 1
Open Access | Times Cited: 98
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: 81
Research on bearing fault diagnosis method based on transformer neural network
Zhuohong Yang, Jian Cen, Xi Liu, et al.
Measurement Science and Technology (2022) Vol. 33, Iss. 8, pp. 085111-085111
Closed Access | Times Cited: 44
Deep multi-scale separable convolutional network with triple attention mechanism: A novel multi-task domain adaptation method for intelligent fault diagnosis
Bo Zhao, Xianmin Zhang, Zhenhui Zhan, et al.
Expert Systems with Applications (2021) Vol. 182, pp. 115087-115087
Closed Access | Times Cited: 48
MPNet: A lightweight fault diagnosis network for rotating machinery
Yi Liu, Ying Chen, Xianguo Li, et al.
Measurement (2024) Vol. 239, pp. 115498-115498
Closed Access | Times Cited: 7
Fault diagnosis of nuclear power plant sliding bearing-rotor systems using deep convolutional generative adversarial networks
Qi Li, Weiwei Zhang, Feiyu Chen, et al.
Nuclear Engineering and Technology (2024) Vol. 56, Iss. 8, pp. 2958-2973
Open Access | Times Cited: 6
Physics-informed Time-Frequency Fusion Network with Attention for Noise-Robust Bearing Fault Diagnosis
Yejin Kim, Young‐Keun Kim
IEEE Access (2024) Vol. 12, pp. 12517-12532
Open Access | Times Cited: 5
Fault Diagnosis of PMSG Stator Inter-Turn Fault Using Extended Kalman Filter and Unscented Kalman Filter
Waseem El Sayed, Mostafa Abd El Geliel, Ahmed Lotfy
Energies (2020) Vol. 13, Iss. 11, pp. 2972-2972
Open Access | Times Cited: 41
Structural Rotor Fault Diagnosis Using Attention-Based Sensor Fusion and Transformers
Aneesh G. Nath, Sandeep S. Udmale, Divyanshu Raghuwanshi, et al.
IEEE Sensors Journal (2021) Vol. 22, Iss. 1, pp. 707-719
Closed Access | Times Cited: 33
Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network
Zhunan Shen, Xiangwei Kong, Liu Cheng, et al.
Sensors (2024) Vol. 24, Iss. 4, pp. 1290-1290
Open Access | Times Cited: 4
Fault Diagnosis of Industrial Robot Based on Multi-Source Data Fusion and Channel Attention Convolutional Neural Networks
Yiwen Zhang, Zhao-Yan Zang, Xinming Zhang, et al.
IEEE Access (2024) Vol. 12, pp. 82247-82260
Open Access | Times Cited: 4
Multiscale Convolutional Neural Network Based on Channel Space Attention for Gearbox Compound Fault Diagnosis
Qinghong Xu, Hong Jiang, Xiangfeng Zhang, et al.
Sensors (2023) Vol. 23, Iss. 8, pp. 3827-3827
Open Access | Times Cited: 11
Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory
Lanjun Wan, Hongyang Li, Yiwei Chen, et al.
Energies (2020) Vol. 13, Iss. 5, pp. 1094-1094
Open Access | Times Cited: 27
Bearing Fault Diagnosis Based on Multi-Scale CNN and Bidirectional GRU
Taher Saghi, Danyal Bustan, Sumeet S. Aphale
Vibration (2022) Vol. 6, Iss. 1, pp. 11-28
Open Access | Times Cited: 18
Adaptive multiscale convolutional neural network model for chemical process fault diagnosis
Ruoshi Qin, Jinsong Zhao
Chinese Journal of Chemical Engineering (2022) Vol. 50, pp. 398-411
Closed Access | Times Cited: 17
Comparative assessment of artificial intelligence (AI)-based algorithms for detection of harmful bloom-forming algae: an eco-environmental approach toward sustainability
Ashish Gaur, Gaurav Pant, Anand Singh Jalal
Applied Water Science (2023) Vol. 13, Iss. 5
Open Access | Times Cited: 8
A dual attention mechanism network with self-attention and frequency channel attention for intelligent diagnosis of multiple rolling bearing fault types
Wenxing Zhang, Jianhong Yang, Xinyu Bo, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 3, pp. 036112-036112
Closed Access | Times Cited: 8
Multi-sensor signals with parallel attention convolutional neural network for bearing fault diagnosis
Zhikai Xing, Yongbao Liu, Qiang Wang, et al.
AIP Advances (2022) Vol. 12, Iss. 7
Open Access | Times Cited: 13
Rolling bearing fault diagnosis method based on MTF-MFACNN
Chunli Lei, Chengxiang Miao, Huiyuan Wan, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 3, pp. 035007-035007
Closed Access | Times Cited: 7
A Deployment Method for Motor Fault Diagnosis Application Based on Edge Intelligence
Zheng Zhou, Yusong Qiao, Xusheng Lin, et al.
Sensors (2024) Vol. 25, Iss. 1, pp. 9-9
Open Access | Times Cited: 2
A novel rolling bearing fault diagnosis method based on parameter optimization variational mode decomposition with feature weighted reconstruction and multi-target attention convolutional neural networks under small samples
Chaoqun Hu, Yonghua Li, Zhe Chen, et al.
Review of Scientific Instruments (2023) Vol. 94, Iss. 7
Closed Access | Times Cited: 6
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