DAMAGE DETECTION USING LEAST-SQUARES SINGULAR VALUE DECOMPOSITION METHOD AND ITS SENSITIVITY TO EXPERIMENTAL DATA

DAMAGE DETECTION USING LEAST-SQUARES SINGULAR VALUE DECOMPOSITION METHOD AND ITS SENSITIVITY TO EXPERIMENTAL DATA

Duarte, H. V.; Donadon, L. V.; Ribeiro, R. P.

Full Article:

A singular value decomposition algorithm, SVD, is applied to frequency response function matrix to identify damage. A composite beam is used to generate vibration data for a healthy and damaged database. The method employed was named robust singular value decomposition as this procedure uses robust statistics to avoid outlier data, so the analysis can be done in a set of healthy and damaged data. Remarks are made on efficiency of the detection algorithm for different damage levels, and measurement techniques.

A singular value decomposition algorithm, SVD, is applied to frequency response function matrix to identify damage. A composite beam is used to generate vibration data for a healthy and damaged database. The method employed was named robust singular value decomposition as this procedure uses robust statistics to avoid outlier data, so the analysis can be done in a set of healthy and damaged data. Remarks are made on efficiency of the detection algorithm for different damage levels, and measurement techniques.

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DOI: 10.5151/meceng-wccm2012-19748

Referências bibliográficas

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Como citar:

Duarte, H. V.; Donadon, L. V.; Ribeiro, R. P.; "DAMAGE DETECTION USING LEAST-SQUARES SINGULAR VALUE DECOMPOSITION METHOD AND ITS SENSITIVITY TO EXPERIMENTAL DATA", p-4135-4146. In: In Proceedings of the 10th World Congress on Computational Mechanics [= Blucher Mechanical Engineering Proceedings, v. 1, n. 1]. São Paulo: Blucher, 2014.
ISSN 23580828, DOI 10.5151/meceng-wccm2012-19748