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

摘要: Abstract Fault detection for structural health monitoring has been a topic of much research during the last decade. Localization and quantification damages, which are linked to fault isolation, have proven be more challenging, at same time higher practical impact. While damage can essentially handled as data-driven approach, localization require strong connection between data analysis physical models. This paper builds upon hypothesis test that checks if mean Gaussian residual vector - whose parameterization is possible locations become non-zero in faulty state. It shown how location extent inferred robust numerical schemes their estimation derived based on QR decompositions minmax approaches. Finally, relevance approach assessed simulations two structures.