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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:

Rolling Bearing Fault Diagnosis Using Modified LFDA and EMD With Sensitive Feature Selection
Xiao Yu, Fei Dong, Enjie Ding, et al.
IEEE Access (2017) Vol. 6, pp. 3715-3730
Open Access | Times Cited: 139

Showing 1-25 of 139 citing articles:

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: 591
Bearing fault diagnosis base on multi-scale CNN and LSTM model
Xiaohan Chen, Beike Zhang, Dong Gao
Journal of Intelligent Manufacturing (2020) Vol. 32, Iss. 4, pp. 971-987
Closed Access | Times Cited: 412
Fault diagnosis of single-phase induction motor based on acoustic signals
Adam Głowacz
Mechanical Systems and Signal Processing (2018) Vol. 117, pp. 65-80
Closed Access | Times Cited: 315
Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions
Bo Zhao, Xianmin Zhang, Hai Li, et al.
Knowledge-Based Systems (2020) Vol. 199, pp. 105971-105971
Closed Access | Times Cited: 299
Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review
Mohd Syahril Ramadhan Mohd Saufi, Zair Asrar Ahmad, M. Salman Leong, et al.
IEEE Access (2019) Vol. 7, pp. 122644-122662
Open Access | Times Cited: 226
Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016
Xiaohan Chen, Rui Yang, Yihao Xue, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-21
Open Access | Times Cited: 185
Deep Decoupling Convolutional Neural Network for Intelligent Compound Fault Diagnosis
Ruyi Huang, Yixiao Liao, Shaohui Zhang, et al.
IEEE Access (2018) Vol. 7, pp. 1848-1858
Open Access | Times Cited: 179
Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine
Laohu Yuan, Dongshan Lian, Kang Xue, et al.
IEEE Access (2020) Vol. 8, pp. 137395-137406
Open Access | Times Cited: 128
Time-Frequency Squeezing and Generalized Demodulation Combined for Variable Speed Bearing Fault Diagnosis
Weiguo Huang, Guanqi Gao, Ning Li, et al.
IEEE Transactions on Instrumentation and Measurement (2018) Vol. 68, Iss. 8, pp. 2819-2829
Closed Access | Times Cited: 126
Actual bearing compound fault diagnosis based on active learning and decoupling attentional residual network
Yanrui Jin, Chengjin Qin, Yixiang Huang, et al.
Measurement (2020) Vol. 173, pp. 108500-108500
Closed Access | Times Cited: 116
A bearing fault diagnosis model based on CNN with wide convolution kernels
Xudong Song, Yuyang Cong, Yifan Song, et al.
Journal of Ambient Intelligence and Humanized Computing (2021) Vol. 13, Iss. 8, pp. 4041-4056
Closed Access | Times Cited: 99
An Improved Method for Pipeline Leakage Localization With a Single Sensor Based on Modal Acoustic Emission and Empirical Mode Decomposition With Hilbert Transform
Changhang Xu, Shasha Du, Piao Gong, et al.
IEEE Sensors Journal (2020) Vol. 20, Iss. 10, pp. 5480-5491
Closed Access | Times Cited: 98
Deep Feature Generating Network: A New Method for Intelligent Fault Detection of Mechanical Systems Under Class Imbalance
Tongyang Pan, Jinglong Chen, Jingsong Xie, et al.
IEEE Transactions on Industrial Informatics (2020) Vol. 17, Iss. 9, pp. 6282-6293
Closed Access | Times Cited: 77
A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions
Yanrui Jin, Chengjin Qin, ZhiNan Zhang, et al.
Science China Technological Sciences (2022) Vol. 65, Iss. 11, pp. 2551-2563
Closed Access | Times Cited: 44
Intelligent Fault Diagnosis of Rolling Bearings Using Efficient and Lightweight ResNet Networks Based on an Attention Mechanism (September 2022)
Meng Chang, Dechen Yao, Jianwei Yang
IEEE Sensors Journal (2023) Vol. 23, Iss. 9, pp. 9136-9145
Closed Access | Times Cited: 27
Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor
Jialin Li, Xueyi Li, David He, et al.
Journal of Intelligent Manufacturing (2020) Vol. 31, Iss. 8, pp. 1899-1916
Closed Access | Times Cited: 62
Concurrent Fault Diagnosis Based on Bayesian Discriminating Analysis and Time Series Analysis With Dimensionless Parameters
Aisong Qin, Qin Hu, Yunrong Lv, et al.
IEEE Sensors Journal (2018) Vol. 19, Iss. 6, pp. 2254-2265
Closed Access | Times Cited: 59
A method for rapidly evaluating reliability and predicting remaining useful life using two-dimensional convolutional neural network with signal conversion
Qibin Wang, Bo Zhao, Hongbo Ma, et al.
Journal of Mechanical Science and Technology (2019) Vol. 33, Iss. 6, pp. 2561-2571
Closed Access | Times Cited: 56
GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction
Jiakai Ding, Liangpei Huang, Dongming Xiao, et al.
Sensors (2020) Vol. 20, Iss. 7, pp. 1946-1946
Open Access | Times Cited: 54
Enhanced bearing fault detection using multichannel, multilevel 1D CNN classifier
Ibrahim Halil Ozcan, Özer Can Devecioğlu, Türker İnce, et al.
Electrical Engineering (2021) Vol. 104, Iss. 2, pp. 435-447
Closed Access | Times Cited: 48
A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems
Zhu Huang, Tao Wang, Wei Liu, et al.
Complexity (2021) Vol. 2021, Iss. 1
Open Access | Times Cited: 43
Feature Extraction Using Hierarchical Dispersion Entropy for Rolling Bearing Fault Diagnosis
Qiang Xue, Boyu Xu, Changbo He, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-11
Closed Access | Times Cited: 41
A New Cross-Domain Bearing Fault Diagnosis Framework Based on Transferable Features and Manifold Embedded Discriminative Distribution Adaption Under Class Imbalance
Xiao Yu, Hongshen Yin, Li Sun, et al.
IEEE Sensors Journal (2023) Vol. 23, Iss. 7, pp. 7525-7545
Closed Access | Times Cited: 20
CFI-LFENet: Infusing cross-domain fusion image and lightweight feature enhanced network for fault diagnosis
Chao Lian, Yuliang Zhao, Jinliang Shao, et al.
Information Fusion (2023) Vol. 104, pp. 102162-102162
Closed Access | Times Cited: 19
An improved rolling bearing fault diagnosis method using DenseNet-BLSTM
Kaihui Zhao, Sicheng Wu
Signal Image and Video Processing (2024) Vol. 18, Iss. S1, pp. 267-276
Closed Access | Times Cited: 5
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