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