期刊
2022 IEEE SENSORS
卷 -, 期 -, 页码 -出版社
IEEE
DOI: 10.1109/SENSORS52175.2022.9967175
关键词
Digital Twin; fault tolerance; neural networks; sensor validation
资金
- Research Council of Norway [311902]
This study focuses on a modular SFDIA architecture and explores the impact of using different types of neural-network building blocks.
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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