Exploring a Modular Architecture for Sensor Validation in Digital Twins

Darvishi, Hossein; Ciuonzo, Domenico; Salvo Rossi, Pierluigi

Darvishi, Hossein; Ciuonzo, Domenico; Salvo Rossi, Pierluigi

Peer reviewed, Journal article

Accepted version

Åpne

Norwegian_University_of_Science_and_Technology_PhD_thesis_template+%281%2911.pdf (662.0Kb)

Permanent lenke

https://hdl.handle.net/11250/3057897

Utgivelsesdato

2022

Sammendrag

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.

Utgiver

IEEE, Institute of Electrical and Electronics Engineers

Tidsskrift

Proceedings of IEEE Sensors: conference

Opphavsrett

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