Output-only structural identification of random vibrating systems

Abstract

A new form to carry out stochastic identification of structures in operational conditions using a non recursive method, the statistic analysis and the wavelet transform, is presented. The statistic analysis contributed to select the best system order and to automation of computational procedures. In general the identification of low frequencies is a difficult task. The wavelet transform is an essential tool for compression of data making possible the complete identification including low frequencies. In addition it improves the computational efficiency. The study of four degrees of freedom simulated system is presented and the results are compared with the analytical modal parameters.

Identification of aeroelastic parameters for helicopter tail rotor limit cycle oscillation monitoring

“…Based on these assumptions, the following properties exist (Mucheroni and Cardoso, 2006; Peeters and Rodeck, 1999)…”

Statistical evaluation of flutter boundaries from flight flutter test data

“…The covariance matrix D 0 , whose diagonal values are the variances of a i parameters, is defined as where , is the expected value of the residual term. The assumption of Gaussian distributed uncorrelated noise data has been made in numerous studies (8,19,21,23,29,(37)(38)(39)(40)(41)(42)(43) and the expected value becomes where  2 e represents the variance of the noise signal, thus…”