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The relative wavelet entropy (RWE) is a commonly considered in the literature damage-sensitive index derived by wavelet transforming linear response acceleration signals from healthy/reference and damaged states of a given structure subject to broadband excitation. Herein, four different energy-preserving wavelet analysis filter banks are employed to compute the RWE for two benchmark structures via algorithms that may efficiently run on-board wireless sensors for decentralised structural health monitoring. It is shown that filter banks of wavelet bases compactly supported in the frequency domain are advantageous since they achieve enhanced frequency selectivity among scales and, therefore, the scale/frequency dependent contributors to the RWE become easier to interpret. Moreover, it is demonstrated that 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, while non-constant Q analysis filter banks are most effective for RWE-based stationary damage detection.

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