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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:

Knowledge extraction and insertion to deep belief network for gearbox fault diagnosis
Jianbo Yu, Guoliang Liu
Knowledge-Based Systems (2020) Vol. 197, pp. 105883-105883
Closed Access | Times Cited: 100

Showing 1-25 of 100 citing articles:

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: 251
A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems
Ting Huang, Qiang Zhang, Xiaoan Tang, et al.
Artificial Intelligence Review (2021) Vol. 55, Iss. 2, pp. 1289-1315
Closed Access | Times Cited: 174
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: 93
Prior Knowledge-Augmented Self-Supervised Feature Learning for Few-Shot Intelligent Fault Diagnosis of Machines
Tianci Zhang, Jinglong Chen, Shuilong He, et al.
IEEE Transactions on Industrial Electronics (2022) Vol. 69, Iss. 10, pp. 10573-10584
Closed Access | Times Cited: 74
Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review
Shaohua Qiu, Xiaopeng Cui, Zuowei Ping, et al.
Sensors (2023) Vol. 23, Iss. 3, pp. 1305-1305
Open Access | Times Cited: 71
A two-stage fault diagnosis methodology for rotating machinery combining optimized support vector data description and optimized support vector machine
Jianqun Zhang, Qing Zhang, Xianrong Qin, et al.
Measurement (2022) Vol. 200, pp. 111651-111651
Closed Access | Times Cited: 70
Multi-modal data cross-domain fusion network for gearbox fault diagnosis under variable operating conditions
Yongchao Zhang, Jinliang Ding, Yongbo Li, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108236-108236
Closed Access | Times Cited: 46
Knowledge Driven Machine Learning Towards Interpretable Intelligent Prognostics and Health Management: Review and Case Study
Ruqiang Yan, Zheng Zhou, Zuogang Shang, et al.
Chinese Journal of Mechanical Engineering (2025) Vol. 38, Iss. 1
Open Access | Times Cited: 2
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
Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples
Daoguang Yang, Hamid Reza Karimi, Kangkang Sun
Neural Networks (2021) Vol. 141, pp. 133-144
Closed Access | Times Cited: 91
Deep morphological convolutional network for feature learning of vibration signals and its applications to gearbox fault diagnosis
Zhuang Ye, Jianbo Yu
Mechanical Systems and Signal Processing (2021) Vol. 161, pp. 107984-107984
Closed Access | Times Cited: 85
Interpretable Machine Learning: A brief survey from the predictive maintenance perspective
Simon Vollert, Martin Atzmueller, Andreas Theissler
(2021), pp. 01-08
Closed Access | Times Cited: 76
Modified Gaussian convolutional deep belief network and infrared thermal imaging for intelligent fault diagnosis of rotor-bearing system under time-varying speeds
Xin Li, Haidong Shao, Hongkai Jiang, et al.
Structural Health Monitoring (2021) Vol. 21, Iss. 2, pp. 339-353
Closed Access | Times Cited: 68
Semi-supervised multi-scale attention-aware graph convolution network for intelligent fault diagnosis of machine under extremely-limited labeled samples
Zongliang Xie, Jinglong Chen, Yong Feng, et al.
Journal of Manufacturing Systems (2022) Vol. 64, pp. 561-577
Closed Access | Times Cited: 65
A sparse domain adaption network for remaining useful life prediction of rolling bearings under different working conditions
Mengqi Miao, Jianbo Yu, Zhihong Zhao
Reliability Engineering & System Safety (2021) Vol. 219, pp. 108259-108259
Closed Access | Times Cited: 58
Deep hypergraph autoencoder embedding: An efficient intelligent approach for rotating machinery fault diagnosis
Mingkuan Shi, Chuancang Ding, Rui Wang, et al.
Knowledge-Based Systems (2022) Vol. 260, pp. 110172-110172
Closed Access | Times Cited: 40
Domain fuzzy generalization networks for semi-supervised intelligent fault diagnosis under unseen working conditions
He Ren, Jun Wang, Zhongkui Zhu, et al.
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110579-110579
Closed Access | Times Cited: 30
Early bearing fault diagnosis for imbalanced data in offshore wind turbine using improved deep learning based on scaled minimum unscented kalman filter
Haihong Tang, Kun Zhang, Bing Wang, et al.
Ocean Engineering (2024) Vol. 300, pp. 117392-117392
Closed Access | Times Cited: 14
Enhancing Rolling Bearing Fault Diagnosis in Motors using the OCSSA-VMD-CNN-BiLSTM Model: A Novel Approach for Fast and Accurate Identification
Chang Yong, Guangqing Bao
IEEE Access (2024) Vol. 12, pp. 78463-78479
Open Access | Times Cited: 10
Combination bidirectional long short-term memory and capsule network for rotating machinery fault diagnosis
Tian Han, Ruiyi Ma, Jigui Zheng
Measurement (2021) Vol. 176, pp. 109208-109208
Closed Access | Times Cited: 56
Improved CNN for the diagnosis of engine defects of 2-wheeler vehicle using wavelet synchro-squeezed transform (WSST)
Anil Kumar, Chander Parkash, Yuqing Zhou, et al.
Knowledge-Based Systems (2020) Vol. 208, pp. 106453-106453
Closed Access | Times Cited: 54
Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses
Ahmad Kamal Mohd Nor, Srinivasa Rao Pedapati, Masdi Muhammad, et al.
Sensors (2021) Vol. 21, Iss. 23, pp. 8020-8020
Open Access | Times Cited: 54
Rotating Machinery Fault Diagnosis Through a Transformer Convolution Network Subjected to Transfer Learning
Xinglong Pei, Xiaoyang Zheng, Jinliang Wu
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-11
Closed Access | Times Cited: 50
Efficient federated convolutional neural network with information fusion for rolling bearing fault diagnosis
Zehui Zhang, Xiaobin Xu, Wenfeng Gong, et al.
Control Engineering Practice (2021) Vol. 116, pp. 104913-104913
Closed Access | Times Cited: 49
AKSNet: A novel convolutional neural network with adaptive kernel width and sparse regularization for machinery fault diagnosis
Zhuang Ye, Jianbo Yu
Journal of Manufacturing Systems (2021) Vol. 59, pp. 467-480
Closed Access | Times Cited: 46
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