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PINN-RUL: Physics-Informed Neural Networks for Remaining Useful Life Prediction in Reusable Rocket Engines

open access: yes
This paper proposes a Physics-Informed Neural Network (PINN) framework for remaining useful life (RUL) prediction in reusable rocket engines. The method embeds Coffin-Manson low-cycle fatigue physics and Chaboche viscoplastic constitutive equations directly into the loss function of an LSTM encoder, forcing predictions to remain physically consistent ...
openaire   +2 more sources

Deep learning-based RUL and SOH prediction of lithium-ion batteries using LOCO. [PDF]

open access: yesSci Rep
Ali B   +6 more
europepmc   +1 more source

Industrial LiFePOâ‚„ battery management system dataset for early degradation analysis and remaining useful life prediction. [PDF]

open access: yesData Brief
Ulfat MAB   +7 more
europepmc   +1 more source

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