Hybrid modelling in condition monitoring
DIRECTORS: Diego Galar, Uday Kumar, Oscar Salgado UNIVERSITY: LTU Lulea
In the context of condition based maintenance, diagnosis and prognosis are fundamental tools in order to determine the state of a monitored system and estimate its remaining useful life. The importance of both processes lies in their usefulness to assure appropriate reliability and safety levels. Three different kinds of modelling of systems are used for diagnosis and prognosis: physical, data-driven and symbolic modelling. In this research work, an emphasis has been placed in physical modelling, applied to the field of rolling element bearings. In order to implement the physical modelling, a multi-body model for rolling element bearings has been developed in such a way that the dynamic response of these machine elements can be obtained, whatever the kind of rolling element and the configuration of the bearings. However, this kind of modelling has not been considered as exclusive for the other two options. In fact, this thesis proposes to make the most of physical modelling with the aim of generating synthetic data that complement the data and information that can be obtained from the data-driven and the symbolic approaches. This data and information fusion is known as hybrid modelling.