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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:

Machinery health indicator construction based on convolutional neural networks considering trend burr
Liang Guo, Yaguo Lei, Naipeng Li, et al.
Neurocomputing (2018) Vol. 292, pp. 142-150
Closed Access | Times Cited: 236

Showing 1-25 of 236 citing articles:

Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction
Xiang Li, Zhang We, Qian Ding
Reliability Engineering & System Safety (2018) Vol. 182, pp. 208-218
Closed Access | Times Cited: 457
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: 403
Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism
Xiang Li, Zhang We, Qian Ding
Signal Processing (2019) Vol. 161, pp. 136-154
Closed Access | Times Cited: 323
Predicting Remaining Useful Life of Rolling Bearings Based on Deep Feature Representation and Transfer Learning
Wentao Mao, Jianliang He, Ming J. Zuo
IEEE Transactions on Instrumentation and Measurement (2019) Vol. 69, Iss. 4, pp. 1594-1608
Closed Access | Times Cited: 270
A new bearing fault diagnosis method based on modified convolutional neural networks
Jiangquan Zhang, Yi Sun, Liang Guo, et al.
Chinese Journal of Aeronautics (2019) Vol. 33, Iss. 2, pp. 439-447
Open Access | Times Cited: 250
Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery
Biao Wang, Yaguo Lei, Tao Yan, et al.
Neurocomputing (2019) Vol. 379, pp. 117-129
Closed Access | Times Cited: 233
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: 231
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: 210
Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places
Xiang Li, Zhang We, Nan-Xi Xu, et al.
IEEE Transactions on Industrial Electronics (2019) Vol. 67, Iss. 8, pp. 6785-6794
Closed Access | Times Cited: 181
A Survey of Predictive Maintenance: Systems, Purposes and Approaches
Yongyi Ran, Xin Zhou, Pengfeng Lin, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 176
A Prognostic Model Based on DBN and Diffusion Process for Degrading Bearing
Changhua Hu, Hong Pei, Xiaosheng Si, et al.
IEEE Transactions on Industrial Electronics (2019) Vol. 67, Iss. 10, pp. 8767-8777
Closed Access | Times Cited: 149
Data alignments in machinery remaining useful life prediction using deep adversarial neural networks
Xiang Li, Zhang We, Hui Ma, et al.
Knowledge-Based Systems (2020) Vol. 197, pp. 105843-105843
Closed Access | Times Cited: 146
Modified Deep Autoencoder Driven by Multisource Parameters for Fault Transfer Prognosis of Aeroengine
Zhiyi He, Haidong Shao, Ziyang Ding, et al.
IEEE Transactions on Industrial Electronics (2021) Vol. 69, Iss. 1, pp. 845-855
Closed Access | Times Cited: 142
Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery
Xiang Li, Xu Li, Hui Ma
Mechanical Systems and Signal Processing (2020) Vol. 143, pp. 106825-106825
Closed Access | Times Cited: 137
Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions
Zhang We, Xiang Li, Hui Ma, et al.
Reliability Engineering & System Safety (2021) Vol. 211, pp. 107556-107556
Closed Access | Times Cited: 130
YOLO-SLAM: A semantic SLAM system towards dynamic environment with geometric constraint
Wenxin Wu, Liang Guo, Hongli Gao, et al.
Neural Computing and Applications (2022) Vol. 34, Iss. 8, pp. 6011-6026
Closed Access | Times Cited: 124
A convolutional neural network based degradation indicator construction and health prognosis using bidirectional long short-term memory network for rolling bearings
Yiwei Cheng, Kui Hu, Jun Wu, et al.
Advanced Engineering Informatics (2021) Vol. 48, pp. 101247-101247
Closed Access | Times Cited: 114
Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks
Xiang Li, Yixiao Xu, Naipeng Li, et al.
IEEE/CAA Journal of Automatica Sinica (2022) Vol. 10, Iss. 1, pp. 121-134
Closed Access | Times Cited: 96
Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier
Saeed Rajabi, Mehdi Saman Azari, Stefania Santini, et al.
Expert Systems with Applications (2022) Vol. 206, pp. 117754-117754
Open Access | Times Cited: 85
Remaining useful life prediction of bearings by a new reinforced memory GRU network
Jianghong Zhou, Yi Qin, Dingliang Chen, et al.
Advanced Engineering Informatics (2022) Vol. 53, pp. 101682-101682
Closed Access | Times Cited: 84
Bearing remaining useful life prediction with convolutional long short-term memory fusion networks
Shaoke Wan, Xiaohu Li, Yanfei Zhang, et al.
Reliability Engineering & System Safety (2022) Vol. 224, pp. 108528-108528
Closed Access | Times Cited: 68
A novel health indicator for intelligent prediction of rolling bearing remaining useful life based on unsupervised learning model
Zifei Xu, Musa Bashir, Qinsong Liu, et al.
Computers & Industrial Engineering (2023) Vol. 176, pp. 108999-108999
Open Access | Times Cited: 57
A Study on a Probabilistic Method for Designing Artificial Neural Networks for the Formation of Intelligent Technology Assemblies with High Variability
V V Bukhtoyarov, В С Тынченко, Vladimir A. Nelyub, et al.
Electronics (2023) Vol. 12, Iss. 1, pp. 215-215
Open Access | Times Cited: 54
FedCAE: A New Federated Learning Framework for Edge-Cloud Collaboration Based Machine Fault Diagnosis
Yaoxiang Yu, Liang Guo, Hongli Gao, et al.
IEEE Transactions on Industrial Electronics (2023) Vol. 71, Iss. 4, pp. 4108-4119
Closed Access | Times Cited: 52
An anti-noise fault diagnosis approach for rolling bearings based on multiscale CNN-LSTM and a deep residual learning model
Hongming Chen, Wei Meng, Yongjian Li, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 4, pp. 045013-045013
Closed Access | Times Cited: 45
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