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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:

Long short-term memory network with multi-resolution singular value decomposition for prediction of bearing performance degradation
Mengfu He, Youguang Zhou, Yang Li, et al.
Measurement (2020) Vol. 156, pp. 107582-107582
Closed Access | Times Cited: 67

Showing 26-50 of 67 citing articles:

Edge Computing Enabled Production Anomalies Detection and Energy-Efficient Production Decision Approach for Discrete Manufacturing Workshops
Chaoyang Zhang, Weixi Ji
IEEE Access (2020) Vol. 8, pp. 158197-158207
Open Access | Times Cited: 20
Raceway defect analysis of rolling element bearing for detecting slip and correlating the force on rolling element with peak acceleration due to impact
A.P. Patil, B.K. Mishra, S. P. Harsha
Measurement (2021) Vol. 179, pp. 109394-109394
Closed Access | Times Cited: 16
Bayesian Optimization LSTM/bi-LSTM Network With Self-Optimized Structure and Hyperparameters for Remaining Useful Life Estimation of Lathe Spindle Unit
Nikhil M. Thoppil, V. Vasu, C.S.P. Rao
Journal of Computing and Information Science in Engineering (2021) Vol. 22, Iss. 2
Closed Access | Times Cited: 16
Online tracking and prediction of slip ring degradation using chaos theory based on LSTM neural network
Xue Zuo, Rui Zhu, Yuankai Zhou
Measurement Science and Technology (2023) Vol. 34, Iss. 5, pp. 055010-055010
Closed Access | Times Cited: 6
Prediction of rolling bearing performance degradation based on sae and TCN-attention models
Yaping Wang, Dekang Hou, Di Xu, et al.
Journal of Mechanical Science and Technology (2023) Vol. 37, Iss. 4, pp. 1567-1583
Closed Access | Times Cited: 6
A low-frequency fault detection method for low-speed planetary gearbox based on acoustic signals
Jiachi Yao, Chao Liu, Hongjun Wang, et al.
Applied Acoustics (2022) Vol. 195, pp. 108838-108838
Closed Access | Times Cited: 9
Combining first prediction time identification and time-series feature window for remaining useful life prediction of rolling bearings with limited data
Hai Li, Chaoqun Wang
Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability (2023) Vol. 238, Iss. 2, pp. 274-290
Closed Access | Times Cited: 5
Bearing Fault Diagnosis Method Based on Improved Singular Value Decomposition Package
Huibin Zhu, Zhangming He, Yaqi Xiao, et al.
Sensors (2023) Vol. 23, Iss. 7, pp. 3759-3759
Open Access | Times Cited: 5
Signal anomaly identification strategy based on Bayesian inference for nuclear power machinery
Dongdong You, Xiaocheng Shen, Gaojun Liu, et al.
Mechanical Systems and Signal Processing (2021) Vol. 161, pp. 107967-107967
Closed Access | Times Cited: 11
A new noise reduction method based on re-weighted group sparse decomposition and its application in gear fault feature detection
Xianbin Zheng, Junsheng Cheng, Yonghong Nie, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 9, pp. 095022-095022
Closed Access | Times Cited: 4
Advanced Prognostic Models for Bearing Health: A Comparative Analysis of BiLSTM and ANFIS
Abdel wahhab Lourari, Tarak BENKEDJOUH, Bilal El Yousfi
Electrotehnica Electronica Automatica (2024) Vol. 72, Iss. 2, pp. 65-74
Closed Access | Times Cited: 1
Degradation State Assessment of IGBT Module Based on Interpretable LSTM-AE Modeling Under Changing Working Conditions
Feng Xie, Fei Xiao, Xin Tang, et al.
IEEE Journal of Emerging and Selected Topics in Power Electronics (2024) Vol. 12, Iss. 6, pp. 5544-5557
Closed Access | Times Cited: 1
Performance degradation prediction of rolling bearing based on temporal graph convolutional neural network
Yaping Wang, Zunshan Xu, Songtao Zhao, et al.
Journal of Mechanical Science and Technology (2024) Vol. 38, Iss. 8, pp. 4019-4036
Closed Access | Times Cited: 1
LSTM-based neural network architecture for predicting the nonlinear dynamic behavior of functional gradient viscoelastic porous plates
Mohamed Janane Allah, S. Hassouna, Abdelaziz Timesli, et al.
Materials Today Communications (2024), pp. 111269-111269
Closed Access | Times Cited: 1
Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention‐LSTM
Yaping Wang, Chaonan Yang, Di Xu, et al.
Shock and Vibration (2021) Vol. 2021, Iss. 1
Open Access | Times Cited: 10
Bearing performance degradation assessment based on the continuous-scale mathematical morphological particle and feature fusion
Xiaoli Yan, Guiji Tang, Xiaolong Wang
Measurement (2021) Vol. 188, pp. 110571-110571
Closed Access | Times Cited: 10
Using LSTM and PSO techniques for predicting moisture content of poplar fibers by Impulse-cyclone Drying
Feng Chen, Xun Gao, Xinghua Xia, et al.
PLoS ONE (2022) Vol. 17, Iss. 4, pp. e0266186-e0266186
Open Access | Times Cited: 7
Degradation assessment of bearing based on machine learning classification matrix
Satish Kumar, Paras Kumar, Girish Kumar
Eksploatacja i Niezawodnosc - Maintenance and Reliability (2021) Vol. 23, Iss. 2, pp. 395-404
Open Access | Times Cited: 9
Rolling bearing performance degradation assessment based on singular value decomposition-sliding window linear regression and improved deep learning network in noisy environment
Shaojiang Dong, Yang Li, Peng Zhu, et al.
Measurement Science and Technology (2021) Vol. 33, Iss. 4, pp. 045015-045015
Closed Access | Times Cited: 9
Health Condition Identification of Rolling Element Bearing Based on Gradient of Features Matrix and MDDCs-MRSVD
Jiadong Meng, Changfeng Yan, Zonggang Wang, et al.
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-13
Closed Access | Times Cited: 6
An implementation of a hybrid method based on machine learning to identify biomarkers in the Covid-19 diagnosis using DNA sequences
Bihter Daş
Chemometrics and Intelligent Laboratory Systems (2022) Vol. 230, pp. 104680-104680
Open Access | Times Cited: 6
Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR
Yiyan Liu
Applied Sciences (2023) Vol. 13, Iss. 12, pp. 7135-7135
Open Access | Times Cited: 3
A novel continuous delay hidden layer deep belief network and its application in life prediction of rolling bearings
Meiqi Wang, Jiayue Xu, Xiaowei Niu, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 3, pp. 035113-035113
Closed Access | Times Cited: 3
Coupled Hidden Markov Fusion of Multichannel Fast Spectral Coherence Features for Intelligent Fault Diagnosis of Rolling Element Bearings
Hongchao Wang, Chuan Li, Wenliao Du
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-10
Closed Access | Times Cited: 8
A performance degradation prediction approach for turbo-generator bearing considering complex working conditions based on clustering indicator and self-optimized deep learning model
Ran Duan, Jianzhong Zhou, Jie Liu, et al.
Measurement Science and Technology (2020) Vol. 32, Iss. 6, pp. 065103-065103
Closed Access | Times Cited: 6

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