An integrated GRU based real-time prognostic method towards uncertainty quantification

Measurement: Sensors - Tập 18 - Trang 100220 - 2021
Liyue Yan1, Houjun Wang1, Hao Wang1, Zhen Liu1
1University of Electronic Science and Technology of China, Chengdu, China

Tài liệu tham khảo

Martínez-García, 2021, Deep recurrent entropy adaptive model for system reliability monitoring, IEEE Transactions on Industrial Informatics, 17, 839, 10.1109/TII.2020.3007152 Rezaeianjouybari, 2020, Deep learning for prognostics and health management: state of the art, challenges, and opportunities, Measurement, 163, 107929, 10.1016/j.measurement.2020.107929 Zhang, 2020, Deep learning-based prognostic approach for lithium-ion batteries with adaptive time-series prediction and on-line validation, Measurement, 164, 108052, 10.1016/j.measurement.2020.108052 Chen, 2020, A probability density function generator based on neural networks, Phys. Stat. Mech. Appl., 541, 123344, 10.1016/j.physa.2019.123344 Readshaw, 2021, Modeling of turbulent flames with the large eddy simulation–probability density function (LES–PDF) approach, stochastic fields, and artificial neural networks, Phys. Fluids, 33, 10.1063/5.0041122 Carr, 2010, Modeling failure modes for residual life prediction using stochastic filtering theory, IEEE Trans. Reliab., 59, 346, 10.1109/TR.2010.2044607 Cho, 2014 Zhao, 2017, Machine health monitoring using local feature-based gated recurrent unit networks, IEEE Trans. Ind. Electron., 65, 1539, 10.1109/TIE.2017.2733438 Chen, 2019, Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process, Reliab. Eng. Syst. Saf., 185, 372, 10.1016/j.ress.2019.01.006 Broderick, 2018, Posteriors, conjugacy, and exponential families for completely random measures, Bernoulli, 24, 3181, 10.3150/16-BEJ855 Alam, 2010