Interval-Based approach for uncertainty quantification of Energy Consumption modeling in Digital Twin

Abstract

Digital twin (DT) is an emerging technology in the context of digital transformation that enables the monitoring, diagnosis, energy efficiency, and optimization of different systems. The model of DT is a crucial feature for an accurate representation of the physical system. The latter can be complex and dynamic which makes it prone to variability and stochastic behavior. Thus, monitoring through a DT model that gives as an output a single best estimation of the nominal behavior can sometimes be insufficient considering the dynamic properties of the system. For this reason, the current paper intends to present a novel approach for DT modeling through interval models to bound and include the uncertainties inside the model using a statistical approach and Hilbert transform. A case study is presented focusing on the energy consumption of an industrial robot considering the variability of the real process and the measurement noise.