Skip to Main content Skip to Navigation
Conference papers

Hankel matrix-based Mahalanobis distance for fault detection robust towards changes in process noise covariance

Szymon Gres 1 Michael Döhler 1 Laurent Mevel 1 
1 I4S - Statistical Inference for Structural Health Monitoring
Inria Rennes – Bretagne Atlantique , COSYS - Département Composants et Systèmes
Abstract : Statistical subspace-based change detection residuals have been developed to infer a change in the eigenstructure of linear systems. Their statistical properties have been properly evaluated in the case of a known reference and constant noise properties. Previous residuals have favored the family of null space-based approaches, whereas the possibility of using other metrics such as the Mahalanobis distance has been omitted. This paper investigates the development and study of such a norm under the premise of a varying noise covariance. Its statistical properties have been studied and tested on a numerical example of a mechanical system.
Complete list of metadata

https://hal.inria.fr/hal-03292515
Contributor : Michael Döhler Connect in order to contact the contributor
Submitted on : Tuesday, July 20, 2021 - 2:39:12 PM
Last modification on : Friday, June 17, 2022 - 1:27:55 PM
Long-term archiving on: : Thursday, October 21, 2021 - 6:47:29 PM

File

Detection_Sysid.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03292515, version 1

Collections

Citation

Szymon Gres, Michael Döhler, Laurent Mevel. Hankel matrix-based Mahalanobis distance for fault detection robust towards changes in process noise covariance. SYSID 2021 - 19th IFAC Symposium on System Identification, Jul 2021, Padua / Virtual, Italy. pp.1-6. ⟨hal-03292515⟩

Share

Metrics

Record views

25

Files downloads

78