A Framework for the Integration of Hybrid Models in Digital Twin Architectures for PHM
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Abstract
Hybrid model structures combine physics-based and machinelearning
(ML) models to leverage complementary strengths
in prognostics and health management (PHM). While both
hybrid modeling and digital twin (DT) architectures are widely
studied, their structural interaction is rarely addressed systematically.
In practice, hybrid models are often developed
application-specifically, and their structural integration into
DT architectures remains weakly formalized.
This paper analyzes hybrid model structures focusing on their
architectural composition and establishes a requirement-driven
configuration framework based on core PHM constraints. Four
hybrid coupling strategies are classified according to their
structural integration principles and positioned within a design
space defined by structural dominance and integration
depth. Based on this configuration framework, architectural
integration requirements for DT environments are derived and
operationalized in a project-specific DT implementation.
The study contributes (1) a structured classification of hybrid
coupling strategies, (2) a requirement-driven configuration
framework for hybrid model structures in PHM contexts,
and (3) a structured integration workflow for embedding
hybrid models into DT architectures. The results provide
a consistent foundation for managing hybrid model structures
within scalable DT environments.
How to Cite
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Hybrid Modeling, Digital Twin
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