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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 new bearing fault diagnosis method based on modified convolutional neural networks
Jiangquan Zhang, Yi Sun, Liang Guo, et al.
Chinese Journal of Aeronautics (2019) Vol. 33, Iss. 2, pp. 439-447
Open Access | Times Cited: 250

Showing 1-25 of 250 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: 426
A comprehensive review on convolutional neural network in machine fault diagnosis
Jinyang Jiao, Ming Zhao, Jing Lin, et al.
Neurocomputing (2020) Vol. 417, pp. 36-63
Open Access | Times Cited: 403
Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review
Dhiraj Neupane, Jongwon Seok
IEEE Access (2020) Vol. 8, pp. 93155-93178
Open Access | Times Cited: 373
A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Zhiqin Zhu, Yangbo Lei, Guanqiu Qi, et al.
Measurement (2022) Vol. 206, pp. 112346-112346
Closed Access | Times Cited: 283
Meta-learning for few-shot bearing fault diagnosis under complex working conditions
Chuanjiang Li, Shaobo Li, Ansi Zhang, et al.
Neurocomputing (2021) Vol. 439, pp. 197-211
Closed Access | Times Cited: 193
A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis
Wentao Mao, Wushi Feng, Yamin Liu, et al.
Mechanical Systems and Signal Processing (2020) Vol. 150, pp. 107233-107233
Closed Access | Times Cited: 190
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
A new bearing fault diagnosis method based on signal-to-image mapping and convolutional neural network
Jing Zhao, Shaopu Yang, Qiang Li, et al.
Measurement (2021) Vol. 176, pp. 109088-109088
Closed Access | Times Cited: 128
A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication
Dongdong Liu, Lingli Cui, Weidong Cheng
Measurement Science and Technology (2023) Vol. 35, Iss. 1, pp. 012002-012002
Closed Access | Times Cited: 81
An improved GNN using dynamic graph embedding mechanism: A novel end-to-end framework for rolling bearing fault diagnosis under variable working conditions
Zidong Yu, Changhe Zhang, Chao Deng
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110534-110534
Closed Access | Times Cited: 54
A fault diagnosis method using improved prototypical network and weighting similarity-Manhattan distance with insufficient noisy data
Changdong Wang, Jingli Yang, Baoqin Zhang
Measurement (2024) Vol. 226, pp. 114171-114171
Closed Access | Times Cited: 33
Application of Semi-Supervised Learning in Image Classification: Research on Fusion of Labeled and Unlabeled Data
Sai Li, Kou Peng, Miao Ma, et al.
IEEE Access (2024) Vol. 12, pp. 27331-27343
Open Access | Times Cited: 30
TRA-ACGAN: A motor bearing fault diagnosis model based on an auxiliary classifier generative adversarial network and transformer network
Zhaoyang Fu, Zheng Liu, Shuangrui Ping, et al.
ISA Transactions (2024) Vol. 149, pp. 381-393
Closed Access | Times Cited: 21
Vibration Signals Analysis by Explainable Artificial Intelligence (XAI) Approach: Application on Bearing Faults Diagnosis
Han-Yun Chen, Ching‐Hung Lee
IEEE Access (2020) Vol. 8, pp. 134246-134256
Open Access | Times Cited: 132
Condition Monitoring and Fault Diagnosis of Induction Motor
Swapnil Gundewar, Prasad V. Kane
Journal of Vibration Engineering & Technologies (2020) Vol. 9, Iss. 4, pp. 643-674
Closed Access | Times Cited: 120
Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery
Shengnan Tang, Shouqi Yuan, Yong Zhu
IEEE Access (2020) Vol. 8, pp. 149487-149496
Open Access | Times Cited: 114
An intelligent fault diagnosis method for rotor-bearing system using small labeled infrared thermal images and enhanced CNN transferred from CAE
Zhiyi He, Haidong Shao, Zhong Xiang, et al.
Advanced Engineering Informatics (2020) Vol. 46, pp. 101150-101150
Closed Access | Times Cited: 106
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
A Novel Bearing Fault Classification Method Based on XGBoost: The Fusion of Deep Learning-Based Features and Empirical Features
Jingsong Xie, Zhaoyang Li, Zitong Zhou, et al.
IEEE Transactions on Instrumentation and Measurement (2020) Vol. 70, pp. 1-9
Closed Access | Times Cited: 91
A novel intelligent fault diagnosis method of rolling bearing based on two-stream feature fusion convolutional neural network
Feng Xue, Weimin Zhang, Fei Xue, et al.
Measurement (2021) Vol. 176, pp. 109226-109226
Closed Access | Times Cited: 88
Bearing defect size assessment using wavelet transform based Deep Convolutional Neural Network (DCNN)
Anil Kumar, Yuqing Zhou, C.P. Gandhi, et al.
Alexandria Engineering Journal (2020) Vol. 59, Iss. 2, pp. 999-1012
Open Access | Times Cited: 86
A New Structured Domain Adversarial Neural Network for Transfer Fault Diagnosis of Rolling Bearings Under Different Working Conditions
Wentao Mao, Yamin Liu, Ling Ding, et al.
IEEE Transactions on Instrumentation and Measurement (2020) Vol. 70, pp. 1-13
Closed Access | Times Cited: 79
Recent progress of machine learning in flow modeling and active flow control
Yunfei Li, Juntao Chang, Chen Kong, et al.
Chinese Journal of Aeronautics (2021) Vol. 35, Iss. 4, pp. 14-44
Open Access | Times Cited: 75
Rotating machinery fault detection and diagnosis based on deep domain adaptation: A survey
Siyu Zhang, Lei Su, Jiefei Gu, et al.
Chinese Journal of Aeronautics (2021) Vol. 36, Iss. 1, pp. 45-74
Open Access | Times Cited: 73
Bearing degradation assessment and remaining useful life estimation based on Kullback-Leibler divergence and Gaussian processes regression
Prem Shankar Kumar, L. A. Kumaraswamidhas, S. K. Laha
Measurement (2021) Vol. 174, pp. 108948-108948
Closed Access | Times Cited: 66
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