Kalman predictor subspace residual for mechanical system damage detection - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2022

Kalman predictor subspace residual for mechanical system damage detection

Abstract

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.
Fichier principal
Vignette du fichier
Safeprocess2022rev.pdf (549.65 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03722489 , version 1 (13-07-2022)

Identifiers

Cite

Michael Döhler, Qinghua Zhang, Laurent Mevel. Kalman predictor subspace residual for mechanical system damage detection. 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⟩. ⟨hal-03722489⟩
46 View
45 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More