Abstract
In this paper, we review hybrid approaches for fault detection and fault diagnosis (FDD) that combine data-driven analysis with physics-based and knowledge-based models to overcome a lack of data and to increase the FDD accuracy. We categorize these hybrid approaches according to the steps of an extended common workflow for FDD. This gives practitioners indications of which kind of hybrid FDD approach they can use in their application.
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