Effect of frequency domain attributes of wavelet analysis filter banks for structural damage localization using the relative wavelet entropy index

摘要

A novel numerical study is undertaken to assess the influence of the frequency domain (FD) attributes of wavelet analysis filter banks for vibration-based structural damage detection and localization using the relative wavelet entropy (RWE): a damage-sensitive index derived by wavelet transforming linear response acceleration signals from a healthy/reference and a damaged state of a given structure subject to broadband excitations. Four different judicially defined energy-preserving wavelet analysis filter banks are employed to compute the RWE pertaining to two benchmark structures via algorithms which can efficiently run on wireless sensors for decentralized structural health monitoring. It is shown that filter banks of compactly supported in the FD wavelet bases (e.g., Meyer wavelets and harmonic wavelets) perform significantly better than the commonly used in the literature dyadic Haar discrete wavelet transform filter banks since they achieve enhanced frequency selectivity among scales (i.e., minimum overlapping of the frequency bands corresponding to adjacent scales) and, therefore, reduce energy leakage and facilitate the interpretation of numerical results in terms of scale/frequency dependent contributors to the RWE. Moreover, it is demonstrated that dyadic DWT filter banks with large constant Q values (i.e., ratio of effective frequency over effective bandwidth) are better qualified to capture damage information associated with high frequencies. Finally, it is concluded that wavelet analysis filter banks achieving nonconstant Q analysis are most effective for RWE-based stationary damage detection as they are not limited by the dyadic DWT discretization and can target the structural natural frequencies in cases these are a priori known.