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Study of Vibration Based SHM Technologies, Part III: Localization Using Statistical Learning Theory

M.K. HOVGAARD, J.B. HANSEN, A SKAFTE, P. OLSEN, R. BRINCKER

Abstract


A novel approach for damage localization, based on covariance equivalent synthesized data and multi-class pattern recognition is presented. The approach combines the data acquired from the structure in the baseline state with data from an FE model but avoids the task of FE model updating. The method is presented as the second half of a two-step approach to damage detection and localization, but it’s capability of performing one-step detection & localization is demonstrated. The technique is tested on simulated data and it is verified on experimental data of two separate laboratory structures. Three types of modal features, AR coefficients, eigenfrequencies and mode shapes, were combined with four types of classifiers. All three types were found to hold information for damage localization, but frequencies were found to have the best noise rejection. Lastly, the value of detection and localization is discussed and calculated for both the one-step approach, the two-step approach, and for a no-localization approach. Based on the experimental data, the twostep approach outperforms the others.

doi: 10.12783/SHM2015/305


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