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Robust Subspace Based Fault Detection

Michael Döhler 1 Laurent Mevel 1 
1 I4S - Statistical Inference for Structural Health Monitoring
IFSTTAR/COSYS - Département Composants et Systèmes, Inria Rennes – Bretagne Atlantique
Abstract : Subspace methods enjoy some popularity, especially in mechanical engineering, where large model orders have to be considered. In the context of detecting changes in the structural properties and the modal parameters linked to them, some subspace based fault detection residual has been recently proposed and applied successfully. However, most works assume that the unmeasured ambient excitation level during measurements of the structure in the reference and possibly damaged condition stays constant, which is not satisfied by any application. This paper addresses the problem of robustness of such fault detection methods. An efficient subspace-based fault detection test is derived that is robust to excitation change but also to numerical instabilities that could arise easily in the computations. Furthermore, the fault detection test is extended to the Unweighted Principal Component subspace algorithm.
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Submitted on : Tuesday, October 19, 2010 - 1:51:48 PM
Last modification on : Friday, June 17, 2022 - 1:28:08 PM
Long-term archiving on: : Thursday, January 20, 2011 - 2:41:14 AM

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  • HAL Id : inria-00527482, version 1

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Michael Döhler, Laurent Mevel. Robust Subspace Based Fault Detection. [Research Report] RR-7427, INRIA. 2010. ⟨inria-00527482⟩

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