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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 Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
Biao Wang, Yaguo Lei, Naipeng Li, et al.
IEEE Transactions on Reliability (2018) Vol. 69, Iss. 1, pp. 401-412
Closed Access | Times Cited: 1182

Showing 1-25 of 1182 citing articles:

Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
Zhibin Zhao, Tianfu Li, Jingyao Wu, et al.
ISA Transactions (2020) Vol. 107, pp. 224-255
Open Access | Times Cited: 426
Deep separable convolutional network for remaining useful life prediction of machinery
Biao Wang, Yaguo Lei, Naipeng Li, et al.
Mechanical Systems and Signal Processing (2019) Vol. 134, pp. 106330-106330
Closed Access | Times Cited: 307
A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Zhiqin Zhu, Yangbo Lei, Guanqiu Qi, et al.
Measurement (2022) Vol. 206, pp. 112346-112346
Closed Access | Times Cited: 283
A two-stage method based on extreme learning machine for predicting the remaining useful life of rolling-element bearings
Zuozhou Pan, Zong Meng, Zijun Chen, et al.
Mechanical Systems and Signal Processing (2020) Vol. 144, pp. 106899-106899
Closed Access | Times Cited: 245
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
A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings
Yudong Cao, Yifei Ding, Minping Jia, et al.
Reliability Engineering & System Safety (2021) Vol. 215, pp. 107813-107813
Closed Access | Times Cited: 233
A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning
Kun Yu, Tian Ran Lin, Hui Ma, et al.
Mechanical Systems and Signal Processing (2020) Vol. 146, pp. 107043-107043
Closed Access | Times Cited: 230
A hybrid deep-learning model for fault diagnosis of rolling bearings
Xu Yang, Zhixiong Li, Shuqing Wang, et al.
Measurement (2020) Vol. 169, pp. 108502-108502
Closed Access | Times Cited: 222
An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery
Lucas Costa Brito, Gian Antonio Susto, Jorge Nei Brito, et al.
Mechanical Systems and Signal Processing (2021) Vol. 163, pp. 108105-108105
Open Access | Times Cited: 215
Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model
Mingming Yan, Xingang Wang, Bingxiang Wang, et al.
ISA Transactions (2019) Vol. 98, pp. 471-482
Closed Access | Times Cited: 187
Research on Remaining Useful Life Prediction of Rolling Element Bearings Based on Time-Varying Kalman Filter
Lingli Cui, Xin Wang, Huaqing Wang, et al.
IEEE Transactions on Instrumentation and Measurement (2019) Vol. 69, Iss. 6, pp. 2858-2867
Closed Access | Times Cited: 186
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
Fault diagnosis of rolling bearings using an Improved Multi-Scale Convolutional Neural Network with Feature Attention mechanism
Zifei Xu, Chun Li, Yang Yang
ISA Transactions (2020) Vol. 110, pp. 379-393
Open Access | Times Cited: 166
XJTU-SY Rolling Element Bearing Accelerated Life Test Datasets: A Tutorial
Lei Yaguo, Tianyu Han, Biao Wang, et al.
Journal of Mechanical Engineering (2019) Vol. 55, Iss. 16, pp. 1-1
Open Access | Times Cited: 163
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: 147
Time-Reassigned Multisynchrosqueezing Transform for Bearing Fault Diagnosis of Rotating Machinery
Gang Yu, Tian Ran Lin, Zhonghua Wang, et al.
IEEE Transactions on Industrial Electronics (2020) Vol. 68, Iss. 2, pp. 1486-1496
Closed Access | Times Cited: 145
Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks
Zifei Xu, Chun Li, Yang Yang
Applied Soft Computing (2020) Vol. 95, pp. 106515-106515
Open Access | Times Cited: 143
A novel deep convolutional neural network-bootstrap integrated method for RUL prediction of rolling bearing
Cheng‐Geng Huang, Hong‐Zhong Huang, Yan‐Feng Li, et al.
Journal of Manufacturing Systems (2021) Vol. 61, pp. 757-772
Closed Access | Times Cited: 143
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
Entropy Measures in Machine Fault Diagnosis: Insights and Applications
Zhiqiang Huo, Miguel Martínez-García, Yu Zhang, et al.
IEEE Transactions on Instrumentation and Measurement (2020) Vol. 69, Iss. 6, pp. 2607-2620
Open Access | Times Cited: 138
Data-Driven Prognostic Scheme for Bearings Based on a Novel Health Indicator and Gated Recurrent Unit Network
Qing Ni, Jinchen Ji, Ke Feng
IEEE Transactions on Industrial Informatics (2022) Vol. 19, Iss. 2, pp. 1301-1311
Closed Access | Times Cited: 134
Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence
Xiao Han, Zili Wang, Min Xie, et al.
Reliability Engineering & System Safety (2021) Vol. 210, pp. 107560-107560
Closed Access | Times Cited: 133
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
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: 129
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: 120
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