Time-varying spectrum estimation of offshore structure response based on a time-varying autoregressive model

The purpose of this study is to propose and investigate a new approach for extracting spectral information of motion response of offshore structures. The approach is based on applying Time-varying Autoregressive (TVAR) model. This study is virtually unexplored in offshore engineering field. In the literatures, a number of works have shown that spectral content are extracted using Discrete Fourier Transform (DFT) for the frequency-domain analysis. Here, we outline a practical algorithm for TVAR model which uses Expectation-maximization (EM) algorithm based Kalman smoother. Short time Fourier transformation and Hilbert transformation are used as benchmark. The method is then applied to sampled discrete displacements of a fixed platform as a time series generated from field measurements. All the methods reveal that the spectrum characteristics of sampled platform displacement are time- varying frequency and time- varying gain distribution. The results indicate that TVAR model using KS with EM algorithm is superior to other methods in tackling frequency or amplitude modulation and systems that have low frequency dynamics. It is also found out that the mean frequency derived from the Hilbert transform is lower 8.2%, around 4.8% for short time Fourier transformation and 6.2% for TVAR model than the FFT spectrum. © 2012 Asian Network for Scientific Information.