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Exploring a Modular Architecture for Sensor Validation in Digital Twins

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.
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Category

Academic article

Language

English

Author(s)

Affiliation

  • SINTEF Energy Research / Gassteknologi
  • University of Naples 'Federico II'
  • Norwegian University of Science and Technology

Year

2022

Published in

Proceedings of IEEE Sensors

ISSN

1930-0395

Volume

2022

View this publication at Norwegian Research Information Repository