A Comparative Study of Modal Parameter Identification Based on Wavelet and Hilbert-Huang Transforms

Abstract

Modal parameter identification is an important topic in vibration-based structural health monitoring. This paper presents a comparative study of the modal parameter identification of structures based on the continuous wavelet transform (WT) using the modified complex Morlet wavelet function and the improved Hilbert–Huang transform (HHT). Special attention is given to some implementation issues, such as the modal separation and end effect in the WT, the optimal parameter selection of the wavelet function, the new stopping criterion for the empirical mode decomposition and the end effect in the HHT. The capabilities of these two techniques are compared and assessed by using three examples: a numerical simulation for a damped system with two very close modes; an impact test on an experimental model with three well-separated modes; and an ambient vibration test on the Z24-bridge benchmark problem. The results demonstrate that both methods are applicable for the system with well-separated modes when the time-frequency resolutions are sufficiently taken into account. For the system with very close modes, the WT method seems to be more effective than HHT. One reason is that the frequency separation of HHT is partially dependent on the decomposition performance of the preprocess tool. Therefore, if the adjacent frequency components are very close, it is difficult to design appropriate parameters for the filters to separate them clearly.