Fault Isolation and Quantification from Gaussian Residuals with Application to Structural Damage Quantification

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 : Fault detection for structural health monitoring has been a topic of much research during the last decade. Localization and quantification of damages, which are linked to fault isolation, have proven to be more challenging, and at the same time of higher practical impact. While damage detection can be essentially handled as a data-driven approach, localization and quantification require a strong connection between data analysis and physical models. This paper builds upon a hypothesis test that checks if the mean of a Gaussian residual vector – whose parameterization is linked to possible damage locations – has become non-zero in the faulty state. It is shown how the damage location and extent can be inferred and robust numerical schemes for their estimation are derived based on QR decompositions and minmax approaches. Finally, the relevance of the approach is assessed in numerical simulations of two structures.
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Communication dans un congrès
SAFEPROCESS - 9th IFAC Symposium on Fault Detection, Diagnosis and Safety of Technical Processes, Sep 2015, Paris, France. 2015, 〈10.1016/j.ifacol.2015.09.599〉
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Michael Döhler, Laurent Mevel. Fault Isolation and Quantification from Gaussian Residuals with Application to Structural Damage Quantification. SAFEPROCESS - 9th IFAC Symposium on Fault Detection, Diagnosis and Safety of Technical Processes, Sep 2015, Paris, France. 2015, 〈10.1016/j.ifacol.2015.09.599〉. 〈hal-01196080〉

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