
Mathematical Biosciences and Engineering, 2019, 16(2): 862880. doi: 10.3934/mbe.2019040.
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A new ensemble residual convolutional neural network for remaining useful life estimation
1 The State Key Laboratory of Digital Manufacturing Equipment & Technology, School of Mechanical Science & Engineering, Huazhong University of Science & Technology, Wuhan, 430074, China
2 School of Electronic Information & Communications, Huazhong University of Science & Technology, Wuhan, 430074, China
Received: , Accepted: , Published:
Special Issues: Optimization methods in Intelligent Manufacturing
Keywords: residual network; convolutional neural network; ensemble learning; remaining useful life estimation; prognostic health management
Citation: Long Wen, Yan Dong, Liang Gao. A new ensemble residual convolutional neural network for remaining useful life estimation. Mathematical Biosciences and Engineering, 2019, 16(2): 862880. doi: 10.3934/mbe.2019040
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This article has been cited by:
 1. Biao Wang, Yaguo Lei, Tao Yan, Naipeng Li, Liang Guo, Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery, Neurocomputing, 2019, 10.1016/j.neucom.2019.10.064
 2. Tarek Berghout, LeïlaHayet Mouss, Ouahab Kadri, Lotfi Saïdi, Mohamed Benbouzid, Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine, Applied Sciences, 2020, 10, 3, 1062, 10.3390/app10031062
 3. Jinyang Jiao, Ming Zhao, Jing Lin, Kaixuan Liang, A comprehensive review on convolutional neural network in machine fault diagnosis, Neurocomputing, 2020, 417, 36, 10.1016/j.neucom.2020.07.088
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