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Kalman predictor subspace residual for mechanical system damage detection

Authors :
Michael Döhler
Qinghua Zhang
Laurent Mevel
Statistical Inference for Structural Health Monitoring (I4S)
Inria Rennes – Bretagne Atlantique
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Département Composants et Systèmes (COSYS)
Université Gustave Eiffel-Université Gustave Eiffel
Source :
SAFEPROCESS 2022-11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2022-11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, Jun 2022, Pafos, Cyprus. pp.1-6, ⟨10.1016/j.ifacol.2022.07.102⟩
Publication Year :
2022
Publisher :
Elsevier BV, 2022.

Abstract

International audience; For mechanical system structural health monitoring, a new residual generation method is proposed in this paper, inspired by a recent result on subspace system identification. It improves statistical properties of the existing subspace residual, which has been naturally derived from the standard subspace system identification method. Replacing the monitored system state-space model by the Kalman filter one-step ahead predictor is the key element of the improvement in statistical properties, as originally proposed by Verhaegen and Hansson in the design of a new subspace system identification method.

Details

ISSN :
24058963
Volume :
55
Database :
OpenAIRE
Journal :
IFAC-PapersOnLine
Accession number :
edsair.doi.dedup.....e80b9bbb5e637e576b72c59f1a573a36