Fast Bayesian modal parameter identification method based on short-time Fourier transform

In order to describe the time-frequency characteristics of non-stationary response signals, a modal identification method based on fast Bayesian and short-time Fourier transform (STFT) was proposed. This method uses short-time Fourier transform to replace classical Fourier transform to identify modal parameters, so that the characteristics of modal parameters in both time and frequency domain can be obtained. Using the time-domain decomposition decoupling (TDD) technology, the multi-DOF multi-modal response signal was converted into a single-DOF single-modal response signal, thus improving the computational efficiency. The expression of NLLF (negative log-likelihood function) was presented under high signal-to-noise ratios. Numerical examples were used to verify the validity of the proposed fast Bayesian STFT method in identification of time-varying frequencies and damping ratios. In addition, the aeroelastic wind tunnel test data of a large-span flexible photovoltaic support structure and field measured wind-induced acceleration responses of a high-rise building were identified with the proposed method to derive the damping ratio and frequency of the corresponding structures. The results were compared with the ones obtained from the classical continuous wavelet transform and Hilbert-Huang transform, which shows that the identified damping ratio and frequency based on FBST are consistent with theoretical ones for both time-dependent and time-independent simulation signals. For aeroelastic test data of the large-span flexible photovoltaic support structure and field measured acceleration data of the high-rise building, the FBST provides frequencies consistent with ones from the continuous wavelet transform and Hilbert-Huang transform, while the obtained damping ratio shows a large coefficient of varation © 2023 Science Press. All rights reserved.