Damage detection and denoising in two-dimensional structures using curvelet transform by wrapping method

Nicknam, Ahmad; Hosseini, Mir Hamid; Bagheri, Abdollah

doi:10.1007/s00419-011-0527-y

Cited by 14 publications

(8 citation statements)

References 19 publications

(27 reference statements)

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“…Both translational components and rotational ones have been used by wavelet transformation [26] while damage detection by axial components and wavelet had not been used for beams before, and in the present study its usability for damage detection and sensitivity to noise will be verified. It should be noted that longitudinal free vibration mode shape was used for rods to detect damage location by wavelet [26] as well as in walls by Curvelet transform [27].…”

Section: Continuous Wavelet Transform (Cwt)mentioning

confidence: 99%

A two-step method composed of wavelet transform and model updating method for multiple damage diagnosis in beams

Abbasnia¹,

Mirzaei²,

Yousefbeik³

2016

J. vibroeng.

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In the present study, a two-step approach comprised of wavelet transform and model updating method for multiple structural damage localization and quantification in beams is proposed. The first step is commenced with applying wavelet transform to axial components of mode shapes so as to predict where the damages are located. During this step, wavelet transform might mistakenly detect a number of elements as impaired due to both sampling interval and middle support effect. In the next step, by defining a damage sensitive objective function consisting of natural frequencies and mode shapes, damage intensities at predicted locations will be computed via model updating method employing ECBO (Enhanced Colliding Bodies Optimization) algorithm. The problem with mistakenly predicted damaged locations will be addressed by reported damage intensities during the second step. The present study also indicates that this two-step method greatly assists in reducing the number of variables during model updating process leading to more precise results. Three numerical examples with multiple damages and noisy modal data are studied in order to guarantee the efficacy of the method. Moreover, one numerical example is solved with a number of other Meta-Heuristic algorithms including GA (Genetic Algorithm), CSS (Charged System Search), Pattern Search and Cuckoo algorithm whose results are compared to ECBO algorithm with the intention of ensuring the veracity of ECBO results. The results vividly demonstrate that this method is highly efficient even in the presence of noise.

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Section: Continuous Wavelet Transform (Cwt)mentioning

confidence: 99%

A two-step method composed of wavelet transform and model updating method for multiple damage diagnosis in beams

Abbasnia¹,

Mirzaei²,

Yousefbeik³

2016

J. vibroeng.

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“…derivatives (Hamey et al 2004;Bagheri, Ghodrati Amiri, and Seyed Razzaghi 2009;Bagheri et al 2011;Nicknam, Hosseini, and Bagheri 2011;Bai et al 2012;Ghodrati Amiri et al 2014). Although both of these methods are useful in damage localization, their inability to determine damage severity led researchers to develop methods considering both the natural frequencies and mode shapes for the identification of damage (Ge and Lui 2005;Catbas, Brown, and Aktan 2006;Yang and Liu 2007;Yang 2009;Ge, Lui, and Khanse 2010;Zhu, Li, and He 2011;Ghodrati Amiri et al 2013;Sung, Koo, and Jung 2014).…”

Section: Introductionmentioning

confidence: 99%

Optimization-based method for structural damage localization and quantification by means of static displacements computed by flexibility matrix

Hosseinzadeh

Amiri

Koo

2015

Engineering Optimization

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This article presents an effective method for structural damage identification. The damage diagnosis problem is introduced as an optimization problem which is based on computing static displacements by the flexibility matrix. By utilizing this matrix, the complexity of the static displacement measurements in real cases can be overcome. The optimization problem is solved by a fast evolutionary optimization strategy, named the cuckoo optimization algorithm. The performance of the presented method was demonstrated by studying the benchmark problem provided by the IASC-ASCE Task Group on Structural Health Monitoring, and a numerical example of a frame. Moreover, the robustness of the presented approach was investigated in the presence of some prevalent modelling errors, and also when noisy and incomplete modal data are available. Finally, the efficiency of the proposed method was verified by an experimental study of a five-storey shear building structure. All the obtained results show the good performance of the presented method.

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“…The problem was studied by the research groups of Ostachowicz and Cao [ 24 , 25 ], where the authors employed the complex WT with directional Gaussian wavelets to increase the directional sensitivity to structural damage in the investigated homogeneous and heterogeneous plates. Another approach with using the curvelet transform revealing sensitivity to various orientations of damage was proposed by the research group of Bagheri [ 26 , 27 ]. Several studies on the enhancement of directional selectivity to specifically oriented damage in 2D structures was performed by the author’s team [ 28 , 29 , 30 ], where the complex fractional WT and quaternion WT were applied for post-processing mode shapes of investigated structures.…”

Section: Introductionmentioning

confidence: 99%

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

Katunin

2021

Sensors

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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.

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Damage detection and denoising in two-dimensional structures using curvelet transform by wrapping method

Cited by 14 publications

References 19 publications

A two-step method composed of wavelet transform and model updating method for multiple damage diagnosis in beams

A two-step method composed of wavelet transform and model updating method for multiple damage diagnosis in beams

Optimization-based method for structural damage localization and quantification by means of static displacements computed by flexibility matrix

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

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