A Comparative Study of Adaptive Mode Decomposition Methods for Modal Response Extraction

Abstract

Adaptive mode decomposition (AMD) methods have received significant interest in recent years as an effective means for analyzing signals of multi-components and high complexity. This paper investigates the feasibility of integrating AMD methods and modal response extraction, and performs a comparative study of few representative AMD methods including the empirical mode decomposition (EMD), local mean decomposition (LMD), empirical wavelet transform (EWT), variational mode decomposition (VMD), nonlinear mode decomposition (NMD), and adaptive local iterative filtering (ALIF) methods. The fusion of AMD and modal analysis adds adaptivity and flexibility into data processing and helps automate the modal analysis process. The comparative study will provide insights on the advantages and disadvantages of the AMD methods as to the application of modal analysis. In this comparative study, the six representative AMD methods are first applied to the free response of a simulated three-degree-of-freedom (3-DOF) system, to extract the modal responses associated with the three modes. After that, the methods are applied to a measured free-response signal of a polymethyl methacrylate (PMMA) beam to assess their capability of analyzing real signals. Finally, the findings are summarized and conclusions are drawn.