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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 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

Showing 1-25 of 371 citing articles:

A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions
Jun Zhu, Nan Chen, Changqing Shen
Mechanical Systems and Signal Processing (2020) Vol. 139, pp. 106602-106602
Closed Access | Times Cited: 251
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
Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism
Jiusi Zhang, Yuchen Jiang, Shimeng Wu, et al.
Reliability Engineering & System Safety (2022) Vol. 221, pp. 108297-108297
Closed Access | Times Cited: 229
Remaining Useful Life Prediction Using a Novel Feature-Attention-Based End-to-End Approach
Hui Liu, Zhenyu Liu, Weiqiang Jia, et al.
IEEE Transactions on Industrial Informatics (2020) Vol. 17, Iss. 2, pp. 1197-1207
Closed Access | Times Cited: 216
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
Gated Dual Attention Unit Neural Networks for Remaining Useful Life Prediction of Rolling Bearings
Yi Qin, Dingliang Chen, Sheng Xiang, et al.
IEEE Transactions on Industrial Informatics (2020) Vol. 17, Iss. 9, pp. 6438-6447
Closed Access | Times Cited: 195
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies
Adam Thelen, Xiaoge Zhang, Olga Fink, et al.
Structural and Multidisciplinary Optimization (2022) Vol. 65, Iss. 12
Open Access | Times Cited: 182
Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods
Carlos Ferreira, Gil Gonçalves
Journal of Manufacturing Systems (2022) Vol. 63, pp. 550-562
Open Access | Times Cited: 163
Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture
Lu Liu, Xiao Song, Zhetao Zhou
Reliability Engineering & System Safety (2022) Vol. 221, pp. 108330-108330
Closed Access | Times Cited: 157
Deep Learning-Based Forecasting Approach in Smart Grids With Microclustering and Bidirectional LSTM Network
Hamidreza Jahangir, Hanif Tayarani, Saleh Sadeghi Gougheri, et al.
IEEE Transactions on Industrial Electronics (2020) Vol. 68, Iss. 9, pp. 8298-8309
Closed Access | Times Cited: 156
Multicellular LSTM-based deep learning model for aero-engine remaining useful life prediction
Sheng Xiang, Yi Qin, Jun Luo, et al.
Reliability Engineering & System Safety (2021) Vol. 216, pp. 107927-107927
Closed Access | Times Cited: 146
Hierarchical attention graph convolutional network to fuse multi-sensor signals for remaining useful life prediction
Tianfu Li, Zhibin Zhao, Chuang Sun, et al.
Reliability Engineering & System Safety (2021) Vol. 215, pp. 107878-107878
Closed Access | Times Cited: 146
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
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
An integrated multi-head dual sparse self-attention network for remaining useful life prediction
Jiusi Zhang, Xiang Li, Jilun Tian, et al.
Reliability Engineering & System Safety (2023) Vol. 233, pp. 109096-109096
Closed Access | Times Cited: 119
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
TCGL: Temporal Contrastive Graph for Self-Supervised Video Representation Learning
Yang Liu, Keze Wang, Lingbo Liu, et al.
IEEE Transactions on Image Processing (2022) Vol. 31, pp. 1978-1993
Open Access | Times Cited: 113
Application of recurrent neural network to mechanical fault diagnosis: a review
Junjun Zhu, Quan Jiang, Yehu Shen, et al.
Journal of Mechanical Science and Technology (2022) Vol. 36, Iss. 2, pp. 527-542
Closed Access | Times Cited: 105
A prognostic driven predictive maintenance framework based on Bayesian deep learning
Liangliang Zhuang, Ancha Xu, Xiaolin Wang
Reliability Engineering & System Safety (2023) Vol. 234, pp. 109181-109181
Closed Access | Times Cited: 91
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
Bayesian deep-learning for RUL prediction: An active learning perspective
Rong Zhu, Yuan Chen, Weiwen Peng, et al.
Reliability Engineering & System Safety (2022) Vol. 228, pp. 108758-108758
Closed Access | Times Cited: 84
Bi-LSTM-Based Two-Stream Network for Machine Remaining Useful Life Prediction
Ruibing Jin, Zhenghua Chen, Keyu Wu, et al.
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-10
Closed Access | Times Cited: 82
A Parallel Hybrid Neural Network With Integration of Spatial and Temporal Features for Remaining Useful Life Prediction in Prognostics
Jiusi Zhang, Jilun Tian, Minglei Li, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-12
Closed Access | Times Cited: 78
A gated graph convolutional network with multi-sensor signals for remaining useful life prediction
Lei Wang, Hongrui Cao, Hao Xu, et al.
Knowledge-Based Systems (2022) Vol. 252, pp. 109340-109340
Closed Access | Times Cited: 76
A hybrid prognosis scheme for rolling bearings based on a novel health indicator and nonlinear Wiener process
Junyu Guo, Zhiyuan Wang, He Li, et al.
Reliability Engineering & System Safety (2024) Vol. 245, pp. 110014-110014
Open Access | Times Cited: 47
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