A robust singular value decomposition for damage detection under changing operating conditions and structural uncertainties

A technique is proposed to detect damage in structures from measurements taken under different conditions (i.e. different operational excitation levels, geometrical uncertainties and surface treatments of the structure). The method is based on a robust singular value decomposition (RSVD) which will be introduced in this article. Using the RSVD the distance of an observation to the subspace spanned by the intact measurements can be computed. Furthermore, from statistics, a threshold can be determined to automatically decide, based on the observationʹs distance to the subspace, if the observation comes from a damaged or intact sample. The proposed RSVD method is compared with an existing method based on the classical least-squares (LS) SVD. The damage detection method is validated on an aluminium beam with different damage scenarios (a saw cut and a fatigue crack), under several conditions (different beams with small dimensional changes, beams covered with damping material and different operating levels).