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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 Wiener-Process-Model-Based Method for Remaining Useful Life Prediction Considering Unit-to-Unit Variability
Naipeng Li, Yaguo Lei, Tao Yan, et al.
IEEE Transactions on Industrial Electronics (2018) Vol. 66, Iss. 3, pp. 2092-2101
Closed Access | Times Cited: 180

Showing 1-25 of 180 citing articles:

A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions
Cheng‐Geng Huang, Hong‐Zhong Huang, Yan‐Feng Li
IEEE Transactions on Industrial Electronics (2019) Vol. 66, Iss. 11, pp. 8792-8802
Closed Access | Times Cited: 371
Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression
Xiaoyu Li, Zhenpo Wang, Jinying Yan
Journal of Power Sources (2019) Vol. 421, pp. 56-67
Closed Access | Times Cited: 273
Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks
Shuzhi Zhang, Baoyu Zhai, Xu Guo, et al.
Journal of Energy Storage (2019) Vol. 26, pp. 100951-100951
Closed Access | Times Cited: 255
Failure Prognosis and Applications—A Survey of Recent Literature
Mojtaba Kordestani, Mehrdad Saif, Marcos E. Orchard, et al.
IEEE Transactions on Reliability (2019) Vol. 70, Iss. 2, pp. 728-748
Closed Access | Times Cited: 246
A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction
Jialin Li, Xueyi Li, David He
IEEE Access (2019) Vol. 7, pp. 75464-75475
Open Access | Times Cited: 206
Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery
Biao Wang, Yaguo Lei, Naipeng Li, et al.
IEEE Transactions on Industrial Electronics (2020) Vol. 68, Iss. 8, pp. 7496-7504
Closed Access | Times Cited: 196
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
Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective
Yuxin Wen, Md Fashiar Rahman, Honglun Xu, et al.
Measurement (2021) Vol. 187, pp. 110276-110276
Open Access | Times Cited: 146
Challenges in predictive maintenance – A review
Pedro Nunes, José Santos, Eugénio M. Rocha
CIRP journal of manufacturing science and technology (2022) Vol. 40, pp. 53-67
Open Access | Times Cited: 108
Transfer learning algorithms for bearing remaining useful life prediction: A comprehensive review from an industrial application perspective
Jiaxian Chen, Ruyi Huang, Zhuyun Chen, et al.
Mechanical Systems and Signal Processing (2023) Vol. 193, pp. 110239-110239
Closed Access | Times Cited: 101
A hybrid method for cutting tool RUL prediction based on CNN and multistage Wiener process using small sample data
Xiangyu Zhang, Bowen Shi, Bowen Feng, et al.
Measurement (2023) Vol. 213, pp. 112739-112739
Closed Access | Times Cited: 41
Performance Supervised Plant-Wide Process Monitoring in Industry 4.0: A Roadmap
Yuchen Jiang, Shen Yin, Okyay Kaynak
IEEE Open Journal of the Industrial Electronics Society (2020) Vol. 2, pp. 21-35
Open Access | Times Cited: 114
Remaining useful life prediction based on a multi-sensor data fusion model
Naipeng Li, Nagi Gebraeel, Yaguo Lei, et al.
Reliability Engineering & System Safety (2020) Vol. 208, pp. 107249-107249
Closed Access | Times Cited: 103
A Bayesian Deep Learning RUL Framework Integrating Epistemic and Aleatoric Uncertainties
Gaoyang Li, Li Yang, Chi-Guhn Lee, et al.
IEEE Transactions on Industrial Electronics (2020) Vol. 68, Iss. 9, pp. 8829-8841
Closed Access | Times Cited: 96
Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model
Naipeng Li, Nagi Gebraeel, Yaguo Lei, et al.
Reliability Engineering & System Safety (2019) Vol. 186, pp. 88-100
Closed Access | Times Cited: 93
Prediction Interval Estimation of Aeroengine Remaining Useful Life Based on Bidirectional Long Short-Term Memory Network
Chuang Chen, Ningyun Lu, Bin Jiang, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-13
Closed Access | Times Cited: 73
Wiener-based remaining useful life prediction of rolling bearings using improved Kalman filtering and adaptive modification
Yuxiong Li, Xianzhen Huang, Pengfei Ding, et al.
Measurement (2021) Vol. 182, pp. 109706-109706
Closed Access | Times Cited: 69
Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review
Bin He, Long Liu, Dong Zhang
Journal of Computing and Information Science in Engineering (2021) Vol. 21, Iss. 3
Closed Access | Times Cited: 66
A nonlinear-drift-driven Wiener process model for remaining useful life estimation considering three sources of variability
Wennian Yu, Wenbing Tu, Il Yong Kim, et al.
Reliability Engineering & System Safety (2021) Vol. 212, pp. 107631-107631
Closed Access | Times Cited: 65
An adaptive and generalized Wiener process model with a recursive filtering algorithm for remaining useful life estimation
Wennian Yu, Yimin Shao, Jin Xu, et al.
Reliability Engineering & System Safety (2021) Vol. 217, pp. 108099-108099
Closed Access | Times Cited: 64
A remaining life prediction of rolling element bearings based on a bidirectional gate recurrent unit and convolution neural network
Yajun Shang, Xinglu Tang, Zhao Guang-qian, et al.
Measurement (2022) Vol. 202, pp. 111893-111893
Closed Access | Times Cited: 53
A Wiener Process Model With Dynamic Covariate for Degradation Modeling and Remaining Useful Life Prediction
Shuyi Zhang, Qingqing Zhai, Xin Shi, et al.
IEEE Transactions on Reliability (2022) Vol. 72, Iss. 1, pp. 214-223
Closed Access | Times Cited: 50
Adaptive staged remaining useful life prediction method based on multi-sensor and multi-feature fusion
Yuntian Ta, Yan‐Feng Li, Wenan Cai, et al.
Reliability Engineering & System Safety (2022) Vol. 231, pp. 109033-109033
Closed Access | Times Cited: 39
RUL prediction for rolling bearings based on Convolutional Autoencoder and status degradation model
Weiyang Xu, Quan Jiang, Yehu Shen, et al.
Applied Soft Computing (2022) Vol. 130, pp. 109686-109686
Closed Access | Times Cited: 38
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