Subspace-based fault detection robust to changes in the noise covariances

Authors:
Michael DöHler;Laurent Mevel
Affiliations:
-;-
Venue:
Automatica (Journal of IFAC)
Year:
2013

Citing 17
Cited 0

Asymptotic methods in statistical theory

Asymptotic methods in statistical theory
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Early warning of slight changes in systems

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Consistency and relative efficiency of subspace methods

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Paper: A survey of design methods for failure detection in dynamic systems

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Process fault detection based on modeling and estimation methods-A survey

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Consistency and asymptotic normality of some subspace algorithms for systems without observed inputs

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Order estimation for subspace methods

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Analysis of the asymptotic properties of the MOESP type of subspace algorithms

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Brief Subspace-based fault detection algorithms for vibration monitoring

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Asymptotic properties of subspace estimators

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Consistency analysis of some closed-loop subspace identification methods

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On the ill-conditioning of subspace identification with inputs

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Abstract

The detection of changes in the eigenstructure of a linear time invariant system by means of a subspace-based residual function has been proposed previously. While enjoying some success in its applicability in particular in the context of vibration monitoring, the robustness of this framework against changes in the noise properties has not been properly addressed yet. In this paper, a new robust residual is proposed and the robustness of its statistics against changes in the noise covariances is shown. The complete theory for hypothesis testing for fault detection is derived and a numerical illustration is provided.