Fusing physics-based and deep learning models for prognostics

Physics-based and data-driven models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability to complex real-world domains: (1) the incompleteness of physics-based models and (2) the limited representativeness of the training dataset f...

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Vydané v:	Reliability engineering & system safety Ročník 217; s. 107961
Hlavní autori:	Arias Chao, Manuel, Kulkarni, Chetan, Goebel, Kai, Fink, Olga
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Barking Elsevier Ltd 01.01.2022 Elsevier BV
Predmet:	Algorithms Artificial neural networks CMAPSS Datasets Deep learning Drift och underhållsteknik Flight conditions Hybrid model Learning algorithms Machine learning Mathematical models Neural networks Operation and Maintenance Parameters Physics Prognostics Reliability engineering Safety critical Training Turbofan engines Deep learning CMAPSS Hybrid model Prognostics
ISSN:	0951-8320, 1879-0836, 1879-0836
On-line prístup:	Získať plný text
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