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Hankel matrix-based Mahalanobis distance for fault detection robust towards changes in process noise covariance
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.
Hankel matrix-based Mahalanobis distance for fault detection robust towards changes in process noise covariance
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.
Hankel matrix-based Mahalanobis distance for fault detection robust towards changes in process noise covariance
Gres, Szymon (author) / Döhler, Michael (author) / Mevel, Laurent (author)
2021-07-01
Gres , S , Döhler , M & Mevel , L 2021 , ' Hankel matrix-based Mahalanobis distance for fault detection robust towards changes in process noise covariance ' , IFAC-PapersOnLine , vol. 54 , no. 7 , pp. 73-78 . https://doi.org/10.1016/j.ifacol.2021.08.337
Article (Journal)
Electronic Resource
English
DDC:
624
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