Knowledge Transfer between Oscillators and Real Vibrating Structures to Enrich Dynamic Monitoring Datasets

Transfer Learning (TL) techniques can be exploited in engineering structures to overcome the main limit of the data-driven approaches for Dynamic Monitoring, i.e. the lack of a labelled dataset for some structural configuration of the monitored systems. A variety of methods can be implemented, but those that enable heterogeneous TL based on Domain Adaptation have proven to be particularly useful, as they allow knowledge to be transferred within a population composed of a wider range of structures. Among them, the Kernelized Bayesian Transfer Learning (KBTL) can be used to improve the knowledge of a less monitored structure exploiting the knowledge of a more monitored one. In this paper the KBTL is assumed to transfer information from an oscillator to a spatial frame of which few observations are available. This is done by comparing the KBTL performance with those of a Support Vector Machine model generated from data representative only of the spatial frame. Both models are trained on datasets composed by natural frequencies of the two systems estimated at different temperature ranges.