Tradtional data Analysis techniques have been dominaed by the Fourier transform. The main restrictions are that the data should be stationary and long enought, and that the system generating the data must be linear. Unfortunately, when the structure is damaged, such as cracking or yielding, the response process in nonlinear, and consequently, Fourier or Fourier-based transform methods such as wavelet analysis approach, is not capable of capturing the intrinsic nonlinear and nonstationary characteristics of the physical process involved in the vibration data. A new technique, the Empirical Mode decompositon (EMD) method and Hlbert spectral analysis, recently developed at NASA Goodard space Flight Center, has been demonstrated to offer a great potential for nonstationary and nonlinear data analyses for wide engineering applications.
This project is a collaborative research between the University of California, Irvine, and NASA Goddard space Flight Center. It is aimed at further development of the recently proposed EMD method and hilbert spectral analysis for applications to damage detection and system identification of civil infrastructures. In addition, efforts will be made to separate the polluted noises from measured vibration data and to capture the intrinsic linear or nonlinear characteristics of the structural response. Signatures resulting from the energy release due to structural damage will also be identified and used to characterize the extent of structural damage.
The unique approach proposed is based on the innovative and powerful data analysis technique to capture the intrinsic nonlinear and nonstatinary characteristics of the measured vibratin data from which system identification and damagedetection can be made. Teh results of this research project are expected to make a significant advancement of the state-of-the technology in system identification and damage detection of civil infrastructures.
