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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 novel method based on nonlinear auto-regression neural network and convolutional neural network for imbalanced fault diagnosis of rotating machinery
Quan Zhou, Yibing Li, Yu Tian, et al.
Measurement (2020) Vol. 161, pp. 107880-107880
Closed Access | Times Cited: 81

Showing 1-25 of 81 citing articles:

A novel fault diagnosis method of rotating machinery via VMD, CWT and improved CNN
Jun Gu, Yuxing Peng, Hao Lu, et al.
Measurement (2022) Vol. 200, pp. 111635-111635
Closed Access | Times Cited: 85
Application of deep learning to fault diagnosis of rotating machineries
Hao Su, Ling Xiang, Aijun Hu
Measurement Science and Technology (2024) Vol. 35, Iss. 4, pp. 042003-042003
Open Access | Times Cited: 15
A Small Sample Focused Intelligent Fault Diagnosis Scheme of Machines via Multimodules Learning With Gradient Penalized Generative Adversarial Networks
Tianci Zhang, Jinglong Chen, Fudong Li, et al.
IEEE Transactions on Industrial Electronics (2020) Vol. 68, Iss. 10, pp. 10130-10141
Closed Access | Times Cited: 124
Task-Sequencing Meta Learning for Intelligent Few-Shot Fault Diagnosis With Limited Data
Yidan Hu, Ruonan Liu, Xianling Li, et al.
IEEE Transactions on Industrial Informatics (2021) Vol. 18, Iss. 6, pp. 3894-3904
Closed Access | Times Cited: 101
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
Enhanced generative adversarial network for extremely imbalanced fault diagnosis of rotating machine
Rugen Wang, Shaohui Zhang, Zhuyun Chen, et al.
Measurement (2021) Vol. 180, pp. 109467-109467
Closed Access | Times Cited: 77
Adaptive Knowledge Transfer by Continual Weighted Updating of Filter Kernels for Few-Shot Fault Diagnosis of Machines
Saibo Xing, Yaguo Lei, Bin Yang, et al.
IEEE Transactions on Industrial Electronics (2021) Vol. 69, Iss. 2, pp. 1968-1976
Closed Access | Times Cited: 67
A chatter detection method in milling of thin-walled TC4 alloy workpiece based on auto-encoding and hybrid clustering
Yichao Dun, Lida Zhu, Boling Yan, et al.
Mechanical Systems and Signal Processing (2021) Vol. 158, pp. 107755-107755
Closed Access | Times Cited: 66
A novel feature adaptive extraction method based on deep learning for bearing fault diagnosis
Tian Zhang, Shulin Liu, Yuan Wei, et al.
Measurement (2021) Vol. 185, pp. 110030-110030
Closed Access | Times Cited: 62
An anti-noise one-dimension convolutional neural network learning model applying on bearing fault diagnosis
Fengqian Zou, Haifeng Zhang, Shengtian Sang, et al.
Measurement (2021) Vol. 186, pp. 110236-110236
Closed Access | Times Cited: 55
Fault diagnosis of rotating machinery based on combination of Wasserstein generative adversarial networks and long short term memory fully convolutional network
Yibing Li, Weiteng Zou, Li Jiang
Measurement (2022) Vol. 191, pp. 110826-110826
Closed Access | Times Cited: 52
Fault Diagnosis of Rotating Machinery Based on 1D–2D Joint Convolution Neural Network
Wenliao Du, Pengjie Hu, Hongchao Wang, et al.
IEEE Transactions on Industrial Electronics (2022) Vol. 70, Iss. 5, pp. 5277-5285
Closed Access | Times Cited: 42
Bayesian optimization and channel-fusion-based convolutional autoencoder network for fault diagnosis of rotating machinery
Li Zou, Keming Zhuang, Anthony Y. Zhou, et al.
Engineering Structures (2023) Vol. 280, pp. 115708-115708
Closed Access | Times Cited: 30
Cavitation state identification of centrifugal pump based on CEEMD-DRSN
Cui Dai, Siyuan Hu, Yuhang Zhang, et al.
Nuclear Engineering and Technology (2023) Vol. 55, Iss. 4, pp. 1507-1517
Open Access | Times Cited: 27
A generalized method for diagnosing multi-faults in rotating machines using imbalance datasets of different sensor modalities
Rismaya Kumar Mishra, Anurag Choudhary, Shahab Fatima, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 132, pp. 107973-107973
Closed Access | Times Cited: 11
A novel approach for bearings multiclass fault diagnosis fusing multiscale deep convolution and hybrid attention networks
Fule Li, Xinlong Zhao
Measurement Science and Technology (2024) Vol. 35, Iss. 4, pp. 045017-045017
Closed Access | Times Cited: 8
Machinery Fault Diagnosis Based on Deep Learning for Time Series Analysis and Knowledge Graphs
Haiying Liu, Ruizhe Ma, Daiyi Li, et al.
Journal of Signal Processing Systems (2021) Vol. 93, Iss. 12, pp. 1433-1455
Closed Access | Times Cited: 47
Generative adversarial network and transfer-learning-based fault detection for rotating machinery with imbalanced data condition
Jun Li, Yongbao Liu, Qijie Li
Measurement Science and Technology (2021) Vol. 33, Iss. 4, pp. 045103-045103
Closed Access | Times Cited: 47
Fault severity classification of ball bearing using SinGAN and deep convolutional neural network
P. Akhenia, Keval Bhavsar, Jaimin Panchal, et al.
Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science (2021) Vol. 236, Iss. 7, pp. 3864-3877
Closed Access | Times Cited: 41
A novel data-driven method based on sample reliability assessment and improved CNN for machinery fault diagnosis with non-ideal data
Xin Zhang, Haifeng Wang, Bo Wu, et al.
Journal of Intelligent Manufacturing (2022) Vol. 34, Iss. 5, pp. 2449-2462
Closed Access | Times Cited: 33
A novel assessable data augmentation method for mechanical fault diagnosis under noisy labels
Xin Zhang, Bo Wu, Xi Zhang, et al.
Measurement (2022) Vol. 198, pp. 111114-111114
Closed Access | Times Cited: 29
Machine fault detection methods based on machine learning algorithms: A review
Giuseppe Ciaburro
Mathematical Biosciences & Engineering (2022) Vol. 19, Iss. 11, pp. 11453-11490
Open Access | Times Cited: 28
Dynamic model-driven intelligent fault diagnosis method for rotary vector reducers
Junkang Zheng, Hui Wang, Anil Kumar, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 124, pp. 106648-106648
Closed Access | Times Cited: 21
A novel aging prediction method of fuel cell based on empirical mode decomposition and complexity threshold quantitative criterion
Zhuang Tian, Jinhui Wang, Ahmed Al‐Durra, et al.
Journal of Power Sources (2023) Vol. 574, pp. 233120-233120
Closed Access | Times Cited: 19
Role of image feature enhancement in intelligent fault diagnosis for mechanical equipment: A review
Yongjian Sun, Wei Wang
Engineering Failure Analysis (2023) Vol. 156, pp. 107815-107815
Closed Access | Times Cited: 16
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