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Deep learning-based satellite heat reliability analysis:

, In: 12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022),

Bao, K. ; Zheng, X. ; Zhang, X.. - p. None , 2022

Link: https://doi.org/10.1049/icp.2022.2847

RT 
T1 <cTitle>12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022)</cTitle>: T1 <Title>Deep learning-based satellite heat reliability analysis</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10110450&Exemplar=1&LAN=DE
				
A1 Bao, K.
A1 Zheng, X.
A1 Zhang, X.
A1 Yao, W.
YR 2022
K1 artificial satellites
K1 condition monitoring
K1 deep learning (artificial intelligence)
K1 mechanical engineering computing
K1 Petri nets
K1 probability
K1 reliability
K1 data-driven method
K1 data-driven methods
K1 deep learning-based satellite heat reliability analysis
K1 heat source layout problem
K1 loss function
K1 mathematical model
K1 max temperature error
K1 prediction errors
K1 satellite series-parallel component subsystems
K1 surrogate model
K1 temperature field prediction problem
K1 traditional MAE loss
K1 U-net model framework
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2022.2847
DO https://doi.org/10.1049/icp.2022.2847
SF ELIB - SuUB Bremen

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