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 / Darvishi, H.; Ciuonzo, D.; Rossi, P. S.. - 2022-:(2022), pp. 1-4. (Intervento presentato al convegno 2022 IEEE Sensors Conference, SENSORS 2022 tenutosi a Hyatt Regency Dallas, usa nel 2022) [10.1109/SENSORS52175.2022.9967175].
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
