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This paper studies damage detection using structural frequency response functions (FRFs). In practice, one major difficulty of using FRFs for damage detection is that the vibration signatures are inevitably contaminated by noise. Sensitivity to detect damage is severely impaired as abnormality information caused by the damage could be covered up by the relatively high measurement noise. To tackle this issue and to develop a robust damage detection protocol, a feature extraction/de-noising methodology based on principal component analysis (PCA) is implemented. We first establish a feature space of the intact structure by using multiple measurements with noise. Abnormal signature that is different from the baseline signature can then be identified and magnified after signal reconstruction using intact structure features. Essentially, the directionality between an inspected signal and the baseline signal in the feature space is used as index of damage occurrence. Numerical examples demonstrate that, in all cases considered, the new methodology has good accuracy and high sensitivity for structural damage detection. The relation between detectability, damage severity, noise level, and the number of data sets of the intact structure is examined.
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X. Fang and J. Tang "Damage detection by statistical analysis of vibration signature", Proc. SPIE 5765, Smart Structures and Materials 2005: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, (17 May 2005);

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