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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 prognostics: A systematic review from data acquisition to RUL prediction
Yaguo Lei, Naipeng Li, Liang Guo, et al.
Mechanical Systems and Signal Processing (2017) Vol. 104, pp. 799-834
Closed Access | Times Cited: 1844

Showing 1-25 of 1844 citing articles:

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: 1181
Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data
Liang Guo, Yaguo Lei, Saibo Xing, et al.
IEEE Transactions on Industrial Electronics (2018) Vol. 66, Iss. 9, pp. 7316-7325
Closed Access | Times Cited: 988
Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network
Jun Zhu, Nan Chen, Weiwen Peng
IEEE Transactions on Industrial Electronics (2018) Vol. 66, Iss. 4, pp. 3208-3216
Closed Access | Times Cited: 567
A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
Weihua Li, Ruyi Huang, Jipu Li, et al.
Mechanical Systems and Signal Processing (2021) Vol. 167, pp. 108487-108487
Open Access | Times Cited: 520
Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review
Purushottam Gangsar, Rajiv Tiwari
Mechanical Systems and Signal Processing (2020) Vol. 144, pp. 106908-106908
Closed Access | Times Cited: 424
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
Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach
Zhenghua Chen, Min Wu, Rui Zhao, et al.
IEEE Transactions on Industrial Electronics (2020) Vol. 68, Iss. 3, pp. 2521-2531
Closed Access | Times Cited: 394
Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice
Enrico Zio
Reliability Engineering & System Safety (2021) Vol. 218, pp. 108119-108119
Open Access | Times Cited: 372
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
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
Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights
Arman Malekloo, Ekin Özer, Mohammad AlHamaydeh, et al.
Structural Health Monitoring (2021) Vol. 21, Iss. 4, pp. 1906-1955
Open Access | Times Cited: 319
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 novel deep learning method based on attention mechanism for bearing remaining useful life prediction
Yuanhang Chen, Gaoliang Peng, Zhiyu Zhu, et al.
Applied Soft Computing (2019) Vol. 86, pp. 105919-105919
Closed Access | Times Cited: 296
A review of vibration-based gear wear monitoring and prediction techniques
Ke Feng, Jinchen Ji, Qing Ni, et al.
Mechanical Systems and Signal Processing (2022) Vol. 182, pp. 109605-109605
Open Access | Times Cited: 277
Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network
Jun Wu, Kui Hu, Yiwei Cheng, et al.
ISA Transactions (2019) Vol. 97, pp. 241-250
Closed Access | Times Cited: 276
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: 269
Remaining useful lifetime prediction via deep domain adaptation
Paulo Roberto de Oliveira da Costa, Alp Akçay, Yingqian Zhang, et al.
Reliability Engineering & System Safety (2019) Vol. 195, pp. 106682-106682
Open Access | Times Cited: 263
Applications of stochastic resonance to machinery fault detection: A review and tutorial
Zijian Qiao, Yaguo Lei, Naipeng Li
Mechanical Systems and Signal Processing (2019) Vol. 122, pp. 502-536
Closed Access | Times Cited: 255
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: 250
A dual-LSTM framework combining change point detection and remaining useful life prediction
Zunya Shi, Abdallah Chehade
Reliability Engineering & System Safety (2020) Vol. 205, pp. 107257-107257
Closed Access | Times Cited: 248
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 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
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
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
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: 228
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