Application of two-dimensional wavelet transform to detect damage in steel plate structures

An experimental proof-of-concept for damage detection in composite beams using modal analysis has been conducted. The purpose was to demonstrate that damage features can be detected, located, and measured on the surface of a relatively complex thin-wall beam made from composite material. (1) Background: previous work has been limited to the study of simple geometries and materials. (2) Methods: damage detection in the work is based on the accurate measurement of mode shapes and an appropriate design of the detection mesh. Both a method requiring information about the healthy structure and a baseline-free method have been implemented. (3) Results: short crack-type damage features, both longitudinal and transverse, were detected reliably, and the true length of the crack can be estimated from the damage signal. Simultaneous detection of two cracks on the same sample is also possible. (4) This work demonstrates the feasibility of automated damage detection in composite beams using sensor arrays.

“…The mode shape difference (MSD) is obtained by subtracting the mode shape of the undamaged structure

from the undamaged structures’ mode shape

for a given mode r [ 11 ] and averaging over all modes in the range considered [ 3 ].

where N is the number of mode shapes, and

and

are the values of the nodal displacement at nodes

in the rth mode of the undamaged ( u ) and damaged ( d ) structure, respectively.…”

Section: Methodology Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental Detection and Measurement of Crack-Type Damage Features in Composite Thin-Wall Beams Using Modal Analysis

Pacheco-Chérrez

Cárdenas

Probst

2021

“…This is possible due to the high sensitivity of WTs to the mentioned local stiffness decreases caused by a damage. Numerous approaches based on continuous wavelet transform (CWT) and discrete wavelet transform (DWT) were developed to date (see, e.g., [ 6 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]). However, using the mentioned WTs in the 2D SDI problems, namely, in identification of damage in plate-like structures, introduces some limitations in the directional selectivity of the identified damage.…”

Section: Introductionmentioning

confidence: 99%

Performance of Damage Identification Based on Directional Wavelet Transforms and Entopic Weights Using Experimental Shearographic Testing Results

Katunin

2021

The paper aims to analyze the performance of the damage identification algorithms using the directional wavelet transforms, which reveal higher sensitivity for various orientations of spatial damage together with lower susceptibility to noise. In this study, the algorithms based on the dual-tree, the double-density, and the dual-tree double-density wavelet transforms were considered and compared to the algorithm based on the discrete wavelet transform. The performed analyses are based on shearographic experimental tests of a composite plate with artificially introduced damage at various orientations. It was shown that the directional wavelet transforms are characterized by better performance in damage identification problems than the basic discrete wavelet transform. Moreover, the proposed approach based on entropic weights applicable to the resulting sets of the detail coefficients after decomposition of mode shapes can be effectively used for automatic selection and emphasizing those sets of the detail coefficients, which contain relevant diagnostic information about damage. The proposed processing method allows raw experimental results from shearography to be significantly enhanced. The developed algorithms can be successfully implemented in a shearographic testing for enhancement of a sensitivity to damage during routine inspections in various industrial sectors.

“…Digital signal processing is an important part of the engineering field, and the main signal processing methods include Fourier Transform, Windowed Fourier Transform, Short Time Fourier Transform, Wavelet Transform, and Hilbert–Huang Transform. Among them, Wavelet Transform (WT) is the most representative in SHM, and the literature includes the following representative studies: Abdulkareem et al [ 25 ] used two-dimensional Continuous Wavelet Transform (CWT) to decompose the difference value of the first order mode shape of the steel plate before and after the damage, and to detect whether the steel plate was damaged according to the difference after decomposition. Xin et al [ 26 ] pointed out that Improved Empirical Wavelet Transform (IEWT) can effectively identify the modal parameters of the structure in the operating state.…”

Section: Introductionmentioning

confidence: 99%

Seismic Damage Identification Method for Curved Beam Bridges Based on Wavelet Packet Norm Entropy

Deng

Huang

Cao

et al. 2021

Curved beam bridges, whose line type is flexible and beautiful, are an indispensable bridge type in modern traffic engineering. Nevertheless, compared with linear bridges, curved beam bridges have more complex internal forces and deformation due to the curvature; therefore, this type of bridge is more likely to suffer damage in strong earthquakes. The occurrence of damage reduces the safety of bridges, and can even cause casualties and property loss. For this reason, it is of great significance to study the identification of seismic damage in curved beam bridges. However, there is currently little research on curved beam bridges. For this reason, this paper proposes a damage identification method based on wavelet packet norm entropy (WPNE) under seismic excitation. In this method, wavelet packet transform is adopted to highlight the damage singularity information, the norm entropy of wavelet coefficient is taken as a damage characteristic factor, and then the occurrence of damage is characterized by changes in the damage index. To verify the feasibility and effectiveness of this method, a finite element model of Curved Continuous Rigid-Frame Bridges (CCRFB) is established for the purposes of numerical simulation. The results show that the damage index based on WPNE can accurately identify the damage location and characterize the severity of damage; moreover, WPNE is more capable of performing damage location and providing early warning than the method based on wavelet packet energy. In addition, noise resistance analysis shows that WPNE is immune to noise interference to a certain extent. As long as a series of frequency bands with larger correlation coefficients are selected for WPNE calculation, independent noise reduction can be achieved.