Statistical Damage Localization Using Kalman Residuals and Model-Based Sensitivities

Abstract

Damage diagnosis is a central task in Structural Health Monitoring (SHM), involving the detection and localization of damage from measured data. This study develops a sensitivity-based approach for structural damage localization using vibration measurements with a Kalman-based residual. Measurement data from the current state are processed using a Kalman filter constructed from the reference-state model. Damage induces a change in the mean of the residual, which is related to variations in physical structural parameters through a first-order perturbation analysis involving sensitivities with respect to structural parameters from a finite element (FE) model. Damage localization is then formulated as a statistical hypothesis testing problem for the individual structural parameters. Previous formulations relied on the full FE model for the Kalman filter, limiting applicability to realistic SHM problems. The present work instead considers a modally truncated formulation, which can moreover be identified directly from reference-state vibration measurements. This enables the Kalman filter to be constructed from experimentally identified modal parameters rather than FE model-based quantities. The approach is validated numerically on a truss structure.