Covariance-driven structural modal parameter identification based on wavelet transform and singular value decomposition

The wavelet transform is used as time-frequency representation for the modal identification from operational vibration measurements. In this paper, a wavelet transform identification technique, which based on covariance-driven and Singular Value Decomposition (SVD), is discussed. The technique starts with the calculation of covariance from random response of the system that converted into the time-scale domain using a continuous wavelet transform. It can be shown that the ridges of the covariance wavelet coefficient magnitudes contain modal information of the system. By taking SVD of the covariance matrix, the ridges of the covariance wavelet coefficient are extracted and be used to estimate modal parameter of the system. A real bridge under ambient excitations is performed to demonstrate the proposed technique. The results obtained are comparable with those previously obtained from the peak pick method in frequency domain and stochastic subspace identification in time domain. The proposed technique is proved reliable and efficient, which can be used to identify the modal information of a full-size bridge under operational conditions.