TY - GEN
T1 - Generic Hybrid Models for Prognostics of Complex Systems
AU - Bajarunas, Kristupas
AU - Baptista, Marcia
AU - Goebel, Kai
AU - Chao, Manuel Arias
PY - 2023
Y1 - 2023
N2 - Hybrid models combining physical knowledge and machine learning show promise for obtaining accurate and robust prognostic models. However, despite the increased interest in hybrid models in recent years, the proposed solutions tend to be domain-specific. As a result, there is no compelling strategy of what, where, and how physics-derived knowledge can be integrated into deep learning models depending on the available representation of physical knowledge and the quality of data for the development of prognostic models for complex systems. This Ph.D. project aims to develop a general strategy for hybridizing prognostic models by exploring multiple methods to incorporate physical knowledge at various stages of the learning algorithm. The project will prioritize general expert knowledge as the primary source of information, while domain-specific knowledge will serve as an additional feature when applicable.
AB - Hybrid models combining physical knowledge and machine learning show promise for obtaining accurate and robust prognostic models. However, despite the increased interest in hybrid models in recent years, the proposed solutions tend to be domain-specific. As a result, there is no compelling strategy of what, where, and how physics-derived knowledge can be integrated into deep learning models depending on the available representation of physical knowledge and the quality of data for the development of prognostic models for complex systems. This Ph.D. project aims to develop a general strategy for hybridizing prognostic models by exploring multiple methods to incorporate physical knowledge at various stages of the learning algorithm. The project will prioritize general expert knowledge as the primary source of information, while domain-specific knowledge will serve as an additional feature when applicable.
UR - http://www.scopus.com/inward/record.url?scp=85178335230&partnerID=8YFLogxK
U2 - 10.36001/phmconf.2023.v15i1.3805
DO - 10.36001/phmconf.2023.v15i1.3805
M3 - Conference contribution
AN - SCOPUS:85178335230
T3 - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
BT - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
A2 - Kulkarni, Chetan S.
A2 - Roychoudhury, Indranil
PB - Prognostics and Health Management Society
T2 - 15th Annual Conference of the Prognostics and Health Management Society, PHM 2023
Y2 - 28 October 2023 through 2 November 2023
ER -