Structural Modal Parameter Identification Using Auto regressive Moving Average Model Based on Improved Empirical Mode Decomposition

摘要

A novel modal parameter identification method of ARMA model based on improved Empirical Mode Decomposition (IEMD) subject to ambient excitation is presented in this paper. It is able to partly solve the problems of identifying modal parameters in ambient excitation, such as mode mixing and false mode in the classic EMD, only output responses and the difficulty of determining the order of ARMA model. At first, a bandpass filter method is used to pre-process the measured primary signals, and the sum of narrow-band signals is obtained. Then a series of Intrinsic Mode Functions (IMFs) are separated from the processed signals by using EMD. The real IMF is determined by the correlative coefficients between the separated IMFs and the primary signals. Finally, the Natural Excitation Technique (NExT) and ARMA (2,2) model are combined to identify structural modal parameters as soon as the real IMF is obtained. To illustrate its effectiveness, modal parameters of a 7-storey steel frame are identified with the proposed method. The results show that the approach proposed can extract modal parameters effectively, and also has an excellent adaptability.