Damage Identification Using Sensitivity-Enhancing Control and Identified Models

This paper investigates a coherence approach for locating structural damage using modal frequencies and transfer function parameters identified from input-output data using Observer/Kalman filter identification (OKID). Autonomous damage identification using such forward methods generally require (i) a structural model by which to relate measured and predicted modal properties induced by damage, and (ii) good sensitivity of modal parameter changes to damage states. Using the coherence approach, a damage parameter vector comprised of a finite set of modal frequencies and transfer function parameters is hypothesized for each damage case using either identified or analytic structural models. Measured parameter vectors are extracted from experimental input-output data for a damaged structure using OKID and are compared to hypotheses to determine the most likely damage state. The richness of the parameter vector set, which is comprised of high-quality frequency measurements and lower-quality transfer function parameters, is evaluated in order to determine the ability to uniquely localize damage. The method is evaluated experimentally using a three-degree-of-freedom torsional system and a space-frame truss. Damage parameter hypotheses are generated from a model of the healthy structure developed by system identification in the torsional system, and an analytic model is used to generate damage hypotheses for the truss structure. Feedback control laws enhance the parameter vectors by including closed-loop modal frequencies in order to reduce noise sensitivity and improve uniqueness of parameter vector hypotheses to each damage case. Results show improvements in damage identification using damage parameter vectors comprised of open- and closed-loop modal frequencies, even when model error exists in structural models used to form damage parameter vector hypotheses.