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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Bibliographic Details
Published in:	Reliability engineering & system safety Vol. 217; p. 107961
Main Authors:	Arias Chao, Manuel, Kulkarni, Chetan, Goebel, Kai, Fink, Olga
Format:	Journal Article
Language:	English
Published:	Barking Elsevier Ltd 01.01.2022 Elsevier BV
Subjects:	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
Online Access:	Get full text
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