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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:

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

Showing 26-50 of 307 citing articles:

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network
Xianli Liu, Shaoyang Liu, Xuebing Li, et al.
Journal of Manufacturing Systems (2021) Vol. 60, pp. 608-619
Closed Access | Times Cited: 91
Degradation modeling and remaining useful life prediction for dependent competing failure processes
Tao Yan, Yaguo Lei, Naipeng Li, et al.
Reliability Engineering & System Safety (2021) Vol. 212, pp. 107638-107638
Closed Access | Times Cited: 89
Self-Attention ConvLSTM and Its Application in RUL Prediction of Rolling Bearings
Biao Li, Baoping Tang, Lei Deng, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-11
Closed Access | Times Cited: 86
Deep Focus Parallel Convolutional Neural Network for Imbalanced Classification of Machinery Fault Diagnostics
Andongzhe Duan, Liang Guo, Hongli Gao, et al.
IEEE Transactions on Instrumentation and Measurement (2020) Vol. 69, Iss. 11, pp. 8680-8689
Closed Access | Times Cited: 85
LSTM-based multi-layer self-attention method for remaining useful life estimation of mechanical systems
Jun Xia, Yunwen Feng, Cheng Lu, et al.
Engineering Failure Analysis (2021) Vol. 125, pp. 105385-105385
Closed Access | Times Cited: 83
Remaining Useful Life Estimation Under Multiple Operating Conditions via Deep Subdomain Adaptation
Yifei Ding, Minping Jia, Yudong Cao
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-11
Closed Access | Times Cited: 70
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
Rolling Bearing Fault Diagnosis Based on One-Dimensional Dilated Convolution Network With Residual Connection
Haopeng Liang, Xiaoqiang Zhao
IEEE Access (2021) Vol. 9, pp. 31078-31091
Open Access | Times Cited: 63
Autoencoder Quasi-Recurrent Neural Networks for Remaining Useful Life Prediction of Engineering Systems
Yiwei Cheng, Kui Hu, Jun Wu, et al.
IEEE/ASME Transactions on Mechatronics (2021) Vol. 27, Iss. 2, pp. 1081-1092
Closed Access | Times Cited: 63
A data-driven approach based on deep neural networks for lithium-ion battery prognostics
Ahmet Kara
Neural Computing and Applications (2021) Vol. 33, Iss. 20, pp. 13525-13538
Closed Access | Times Cited: 59
Convolutional Transformer: An Enhanced Attention Mechanism Architecture for Remaining Useful Life Estimation of Bearings
Yifei Ding, Minping Jia
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-10
Closed Access | Times Cited: 58
A deep attention residual neural network-based remaining useful life prediction of machinery
Fuchuan Zeng, Yiming Li, Yuhang Jiang, et al.
Measurement (2021) Vol. 181, pp. 109642-109642
Closed Access | Times Cited: 57
A sparse domain adaption network for remaining useful life prediction of rolling bearings under different working conditions
Mengqi Miao, Jianbo Yu, Zhihong Zhao
Reliability Engineering & System Safety (2021) Vol. 219, pp. 108259-108259
Closed Access | Times Cited: 57
An adversarial transfer network with supervised metric for remaining useful life prediction of rolling bearing under multiple working conditions
Jichao Zhuang, Minping Jia, Xiaoli Zhao
Reliability Engineering & System Safety (2022) Vol. 225, pp. 108599-108599
Closed Access | Times Cited: 57
Bearing Remaining Useful Life Prediction Based on Regression Shapalet and Graph Neural Network
Xiaoyu Yang, Ying Zheng, Yong Zhang, et al.
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-12
Closed Access | Times Cited: 55
Rolling bearing remaining useful life prediction based on dilated causal convolutional DenseNet and an exponential model
Wanmeng Ding, Jimeng Li, Weilin Mao, et al.
Reliability Engineering & System Safety (2022) Vol. 232, pp. 109072-109072
Closed Access | Times Cited: 53
The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data
Han Cheng, Xianguang Kong, Qibin Wang, et al.
Reliability Engineering & System Safety (2022) Vol. 225, pp. 108581-108581
Closed Access | Times Cited: 52
RUL prediction of machinery using convolutional-vector fusion network through multi-feature dynamic weighting
Xiaofei Liu, Yaguo Lei, Naipeng Li, et al.
Mechanical Systems and Signal Processing (2022) Vol. 185, pp. 109788-109788
Closed Access | Times Cited: 52
FedRUL: A New Federated Learning Method for Edge-Cloud Collaboration Based Remaining Useful Life Prediction of Machines
Liang Guo, Yaoxiang Yu, Mengui Qian, et al.
IEEE/ASME Transactions on Mechatronics (2022) Vol. 28, Iss. 1, pp. 350-359
Closed Access | Times Cited: 52
Wind turbine fault detection based on deep residual networks
Jiayang Liu, Xiaosun Wang, Shijing Wu, et al.
Expert Systems with Applications (2022) Vol. 213, pp. 119102-119102
Closed Access | Times Cited: 50
Research on a remaining useful life prediction method for degradation angle identification two-stage degradation process
Zhijian Wang, Yuntian Ta, Wenan Cai, et al.
Mechanical Systems and Signal Processing (2022) Vol. 184, pp. 109747-109747
Closed Access | Times Cited: 46
Multi-dimensional recurrent neural network for remaining useful life prediction under variable operating conditions and multiple fault modes
Yiwei Cheng, Chao Wang, Jun Wu, et al.
Applied Soft Computing (2022) Vol. 118, pp. 108507-108507
Closed Access | Times Cited: 40
Remaining useful life estimation of bearings under different working conditions via Wasserstein distance-based weighted domain adaptation
Tao Hu, Yiming Guo, Liudong Gu, et al.
Reliability Engineering & System Safety (2022) Vol. 224, pp. 108526-108526
Closed Access | Times Cited: 40
Incremental Learning for Remaining Useful Life Prediction via Temporal Cascade Broad Learning System With Newly Acquired Data
Yudong Cao, Minping Jia, Peng Ding, et al.
IEEE Transactions on Industrial Informatics (2022) Vol. 19, Iss. 4, pp. 6234-6245
Closed Access | Times Cited: 37
A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks
Huixin Zhang, Xiaopeng Xi, Rong Pan
Reliability Engineering & System Safety (2023) Vol. 237, pp. 109332-109332
Closed Access | Times Cited: 37

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