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The present paper addresses the subject of truss damage identification using measured frequency response functions (FRF). Damage identification matrix is formed using measured FRFs obtained from truss dynamic test. Then using principal component analysis (PCA),the variable space dimensions of damage identification matrix can be reduced,and original data characters of FRFs can be analyzed and extracted from lower dimension variable space. Thus truss damages can be identified using the multivariate control chart of first several order principal components which contain almost all of original data information. Without the need for modal parameters,the method avoids the errors of modal fitting. In order to validate the reliability of the method,a whole size truss was tested with six types of damage case concerning single or two element damages. The experimental result shows that the proposed method is straightforward and reliable for truss damage identification. Especially,the method has good applicability for the truss under noisy environment and non-linear cases.
The present paper addresses the subject of truss damage identification using measured frequency response functions (FRF). Damage identification matrix is formed using measured FRFs obtained from truss dynamic test. Then using principal component analysis (PCA), the variable space dimensions of damage identification matrix can truss damages can be identified using the multivariate control chart of the multivariate control chart of the first several order principal components which contain almost all of the original data information. Without the need for modal parameters, the method avoids the errors of modal fitting. In order to validate the reliability of the method, a whole size truss was tested with six types of damage case concerning single or two element damages. The experimental result shows that the proposed method is straightforward and reliable for truss damage identification. Especially, the method has g ood applicability for the truss under noisy environment and non-linear cases.