Damage Detection in Structures Based on Principal Component Analysis of Forced Harmonic Responses

An approach based on principal component analysis (PCA) is considered here to tackle the problem of structural damage detection. The key idea of PCA is to reduce a large number of measured data to a much smaller number of uncorrelated variables while retaining as much as possible of the variation in the original data. PCA is applied here to the problem of damage detection in structures submitted to harmonic excitation. When processing vibration measurements, it can be shown that the basis of eigenvectors (called the proper orthogonal modes) span the same subspace as the mode-shape vectors of the monitored structure. Thus
the damage detection problem may be solved using the concept of subspace angle between a reference subspace spanned by the eigenvectors of the initial (undamaged) structure and the subspace spanned by the eigenvectors of the current (possibly damaged) structure. The method is illustrated on the example of a real truss structure for damage detection and is combined to a model updating technique for damage localization.