Decision-support systems rely on data exchange between digital twins (DTs) and physical twins (PTs). Faulty sensors (e.g, due to hardware/software failures) deliver unreliable data and potentially generate critical damages. Prompt sensor fault detection, isolation and accommodation (SFDIA) plays a crucial role in DT design. In this respect, data-driven approaches to SFDIA have recently shown to be effective. This work focuses on a modular SFDIA (M-SFDIA) architecture and explores the impact of using different types of neural-network (NN) building blocks. Numerical results of different choices are shown with reference to a wireless sensor network publicly-available dataset demonstrating the validity of such architecture.

Exploring a Modular Architecture for Sensor Validation in Digital Twins

Ciuonzo D.;
2022

Abstract

Decision-support systems rely on data exchange between digital twins (DTs) and physical twins (PTs). Faulty sensors (e.g, due to hardware/software failures) deliver unreliable data and potentially generate critical damages. Prompt sensor fault detection, isolation and accommodation (SFDIA) plays a crucial role in DT design. In this respect, data-driven approaches to SFDIA have recently shown to be effective. This work focuses on a modular SFDIA (M-SFDIA) architecture and explores the impact of using different types of neural-network (NN) building blocks. Numerical results of different choices are shown with reference to a wireless sensor network publicly-available dataset demonstrating the validity of such architecture.
978-1-6654-8464-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/904836
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