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Conference Papers Year : 2021

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

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.
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Dates and versions

hal-03292515 , version 1 (20-07-2021)

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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, ⟨10.1016/j.ifacol.2021.08.337⟩. ⟨hal-03292515⟩
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