Detection of the instantaneous frequency degradation due to damages of a fixed offshore jacket platform using the iEEMD‐based Hilbert Huang transform under a wave excitation

Abstract

In online structural health monitoring of the fixed offshore jacket platform, the determination of the time-dependent degradation of structures due to damages is the most important step in a maintenance strategy. This paper presents the application of the Hilbert Huang transform method based on the iEEMD (improved ensemble empirical mode decomposition) algorithm to identify the instantaneous natural frequency degradation due to damages of the fixed offshore structure under the random wave excitation. The iEEMD algorithm is proposed to restrain the mode mixing and used to adaptively process the big response data of the offshore structure during the wave loading with a wide frequency range. The vibration response measurement experiment for the offshore structure was conducted in a wave tank in this study. The identified fundamental natural frequencies by the iEEMD-based Hilbert Huang method were evaluated and compared with results of the numerical model of the eigenvalue analysis. The main result demonstrates that the instantaneous frequency degradation and nonlinear time-varying behavior due to the brace damage of the fixed offshore jacket platform can be successfully identified by the iEEMD-based Hilbert Huang transform technique under a wave load.damages, fixed offshore jacket platform, iEEMD-based Hilbert Huang transform, instantaneous natural frequency degradation, wave excitation | INTRODUCTIONThe fixed offshore jacket platforms are usually far away from the mainland and greatly affected by the marine environment. The structural degradation due to damages must to be detected, evaluated in time to ensure the safety of the structure during its service life. Therefore, in recent years, the vibration-based structural health monitoring has been interested in monitoring the damages that caused the frequency degradation to propose the suitable maintenance strategy or repair works in time. [1][2][3][4] The identification of dynamic characteristics including natural frequencies, damping ratios, and the mode shape based on the measured response vibration signals of the displacement and acceleration was

Research on improved modal parameter identification method using Hilbert-Huang transform

“…Lan et al 31 used Wavelet and improved Hilbert–Huang transform method to study the spectrum distribution and energy of turbine pressure pulsation. Trung et al 32 proposed the HHT method based on the improved ensemble empirical mode decomposition (iEEMD) algorithm to identify the instantaneous natural frequency degradation due to damages of the fixed offshore structure under the random wave excitation. Arslan and Karhan 33 classified the healthy and diseased phonocardiogram (PCG) signals based on the HHT method and machine learning algorithms.…”