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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:

Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification
Chen Lü, Zhenya Wang, Bo Zhou
Advanced Engineering Informatics (2017) Vol. 32, pp. 139-151
Closed Access | Times Cited: 393

Showing 1-25 of 393 citing articles:

Deep learning and its applications to machine health monitoring
Rui Zhao, Ruqiang Yan, Zhenghua Chen, et al.
Mechanical Systems and Signal Processing (2018) Vol. 115, pp. 213-237
Closed Access | Times Cited: 2047
Applications of machine learning to machine fault diagnosis: A review and roadmap
Yaguo Lei, Bin Yang, Xinwei Jiang, et al.
Mechanical Systems and Signal Processing (2020) Vol. 138, pp. 106587-106587
Open Access | Times Cited: 1976
1D convolutional neural networks and applications: A survey
Serkan Kıranyaz, Onur Avcı, Osama Abdeljaber, et al.
Mechanical Systems and Signal Processing (2020) Vol. 151, pp. 107398-107398
Open Access | Times Cited: 1820
Deep learning for smart manufacturing: Methods and applications
Jinjiang Wang, Yulin Ma, Laibin Zhang, et al.
Journal of Manufacturing Systems (2018) Vol. 48, pp. 144-156
Closed Access | Times Cited: 1365
Deep Learning Algorithms for Bearing Fault Diagnostics—A Comprehensive Review
Shen Zhang, Shibo Zhang, Bingnan Wang, et al.
IEEE Access (2020) Vol. 8, pp. 29857-29881
Open Access | Times Cited: 600
A transfer convolutional neural network for fault diagnosis based on ResNet-50
Long Wen, Xinyu Li, Liang Gao
Neural Computing and Applications (2019) Vol. 32, Iss. 10, pp. 6111-6124
Closed Access | Times Cited: 582
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: 412
An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks
Hongfeng Tao, Peng Wang, Yiyang Chen, et al.
Journal of the Franklin Institute (2020) Vol. 357, Iss. 11, pp. 7286-7307
Closed Access | Times Cited: 247
Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm
Thanasis Kotsiopoulos, Panagiotis Sarigiannidis, Dimosthenis Ioannidis, et al.
Computer Science Review (2021) Vol. 40, pp. 100341-100341
Closed Access | Times Cited: 238
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
Behnoush Rezaeianjouybari, Yi Shang
Measurement (2020) Vol. 163, pp. 107929-107929
Closed Access | Times Cited: 234
Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery
Yumei Qi, Changqing Shen, Dong Wang, et al.
IEEE Access (2017) Vol. 5, pp. 15066-15079
Open Access | Times Cited: 227
Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework
Wei Li, Xiang Zhong, Haidong Shao, et al.
Advanced Engineering Informatics (2022) Vol. 52, pp. 101552-101552
Closed Access | Times Cited: 220
Intelligent fault diagnosis method of planetary gearboxes based on convolution neural network and discrete wavelet transform
Renxiang Chen, Xin Huang, Lixia Yang, et al.
Computers in Industry (2019) Vol. 106, pp. 48-59
Closed Access | Times Cited: 211
A Review on Deep Learning Applications in Prognostics and Health Management
Liangwei Zhang, Jing Lin, Bin Liu, et al.
IEEE Access (2019) Vol. 7, pp. 162415-162438
Open Access | Times Cited: 182
Multitask Convolutional Neural Network With Information Fusion for Bearing Fault Diagnosis and Localization
Sheng Guo, Bin Zhang, Tao Yang, et al.
IEEE Transactions on Industrial Electronics (2019) Vol. 67, Iss. 9, pp. 8005-8015
Closed Access | Times Cited: 181
A Deep Learning Method for Bearing Fault Diagnosis Based on Time-Frequency Image
Jianyu Wang, Zhenling Mo, Heng Zhang, et al.
IEEE Access (2019) Vol. 7, pp. 42373-42383
Open Access | Times Cited: 179
Automatic defect detection of metro tunnel surfaces using a vision-based inspection system
Dawei Li, Qian Xie, Xiaoxi Gong, et al.
Advanced Engineering Informatics (2020) Vol. 47, pp. 101206-101206
Closed Access | Times Cited: 166
An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder
Changqing Shen, Yumei Qi, Jun Wang, et al.
Engineering Applications of Artificial Intelligence (2018) Vol. 76, pp. 170-184
Closed Access | Times Cited: 162
Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks
Shijie Hao, Feng‐Xiang Ge, Yanmiao Li, et al.
Measurement (2020) Vol. 159, pp. 107802-107802
Closed Access | Times Cited: 162
Rolling bearing fault diagnosis with combined convolutional neural networks and support vector machine
Tian Han, Longwen Zhang, Zhongjun Yin, et al.
Measurement (2021) Vol. 177, pp. 109022-109022
Closed Access | Times Cited: 162
Unsupervised cross-domain rolling bearing fault diagnosis based on time-frequency information fusion
Hongfeng Tao, Jier Qiu, Yiyang Chen, et al.
Journal of the Franklin Institute (2022) Vol. 360, Iss. 2, pp. 1454-1477
Closed Access | Times Cited: 160
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: 139
A Time Series Transformer based method for the rotating machinery fault diagnosis
Yuhong Jin, Lei Hou, Yushu Chen
Neurocomputing (2022) Vol. 494, pp. 379-395
Closed Access | Times Cited: 134
Imbalanced fault diagnosis of rolling bearing using improved MsR-GAN and feature enhancement-driven CapsNet
Jie Liu, Changhe Zhang, Xingxing Jiang
Mechanical Systems and Signal Processing (2021) Vol. 168, pp. 108664-108664
Closed Access | Times Cited: 133
Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals
Bayu Adhi Tama, Malinda Vania, Seung‐Chul Lee, et al.
Artificial Intelligence Review (2022) Vol. 56, Iss. 5, pp. 4667-4709
Open Access | Times Cited: 128
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