A Feature of Mechanics-Driven Statistical Moments of Wavelet Transform-Processed Dynamic Responses for Damage Detection in Beam-Type Structures

Abstract

Multiple damage detection using structural responses only is a problem unresolved that is in the field of structural health monitoring. To address this problem, a novel feature of mechanics-driven statistical moments of wavelet transform-processed dynamic responses is proposed for multi-damage identification in beam-type structures. This feature is referred to as a continuous wavelet transform (CWT)-second-order strain statistical moment (SSSM), with CWT-SSSM in the abbreviation. The mechanical connotation of CWT-SSSM lies in that the SSSM of each order principal vibration contains strain mode shapes, inducing greater sensitivity to local damage. With this method, the CWT is used to extract and amplify the singularities caused by damage in each order SSSM curve, following which the data fusion technology and three-sigma rule in statistics are adopted to construct the damage index. The presence of damage is characterized by the abrupt change in the damage index. The soundness and characteristics of the CWT-SSSM feature are verified by identifying multiple damages in a cantilever beam bearing two breathing cracks. The results show that the proposed feature can accurately designate multiple cracks free of baseline information on the intact counterpart; moreover, it has robustness against noise and applicability under excitations of approximately uniform spectra.

Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection

“…The wavelet transform inherits and develops the idea of localization of the Fourier transform, while overcoming the disadvantages of the window size not varying with frequency. Its main feature is to highlight the characteristics of certain aspects of the signal after the transform, and to gradually refine the signal on multiple scales through the telescopic translation operation, eventually achieving time subdivision at high frequencies and frequency subdivision at low frequencies, which can automatically adapt to the requirements of time-frequency signal analysis and thus focus on arbitrary details of the signal [23][24][25]. Therefore, wavelet transform has been successfully applied to many fields, especially the discrete numerical algorithm of wavelet transform, which is widely used in the study of many theoretical problems.…”

Damage Identification in Reinforced Concrete Beams Using Wavelet Transform of Modal Excitation Responses

“…Therefore, this more sensitive indicator based on C-PSD is expected to be more effective in analyzing and detecting structural damages than previous indicators presented just based on basic statistical parameters, including the average and standard deviation. Huang et al [12] used a mechanics-driven statistical moment feature of wavelet transformprocessed dynamic responses to suggest a technique for detecting various kinds of damage in beam-type structures. CWT, which stands for continuous wavelet transform, is used to represent the feature of analyzing the second-order strain statistical moment (SSSM).…”