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AuthorDoostmohammadian, M.
AuthorMeskin, Nader
Available date2022-04-14T08:45:40Z
Publication Date2020
Publication NameIEEE Transactions on Control of Network Systems
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/TCNS.2020.3029165
URIhttp://hdl.handle.net/10576/29780
AbstractThis paper considers the problem of simultaneous sensor fault detection, isolation, and networked estimation of linear full-rank dynamical systems. The proposed networked estimation is a variant of single time-scale protocol and is based on (i) consensus on a-priori estimates and (ii) measurement innovation. The necessary connectivity condition on the sensor network and stabilizing block-diagonal gain matrix is derived based on our previous works. Considering additive faults in the presence of system and measurement noise, the estimation error at sensors is derived and proper residuals are defined for fault detection. Unlike many works in the literature, no simplifying upper-bound condition on the noise is considered and we assume Gaussian system/measurement noise. A probabilistic threshold is then defined for fault detection based on the estimation error covariance norm. Finally, a graph-theoretic sensor replacement scenario is proposed to recover possible loss of networked observability due to removing the faulty sensor. We examine the proposed fault detection and isolation scheme on an illustrative academic example to verify the results and make a comparison IEEE
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectDynamical systems
Error detection
Graph theory
Sensor networks
A-priori estimates
Estimation errors
Fault detection and isolation schemes
Gaussian systems
Graph-theoretic
Measurement Noise
Networked estimations
Sensor fault detection
Fault detection
TitleSensor Fault Detection and Isolation via Networked Estimation: Full-Rank Dynamical Systems
TypeArticle
dc.accessType Abstract Only


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