Minimum variance Lamb wave imaging based on weighted sparse decomposition coefficients in quasi-isotropic composite laminates

Xu, Caibin; Yang, Zhibo; Zuo, Hao; Deng, Mingxi

doi:10.1016/j.compstruct.2021.114432

Cited by 18 publications

(7 citation statements)

References 27 publications

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“…Therefore, most of the entries in damage index should be zero, and only the entries for damaged elements are nonzero. In other words, the damage index is a sparse vector (Wang et al, 2022; Xu et al, 2021). In light of sparse recovery theory, the sparse damage index can be accurately recovered through sparse regularization.…”

Section: Introductionmentioning

confidence: 99%

Substructural damage identification using autoregressive moving average with exogenous inputs model and sparse regularization

Zhu

Weng

et al. 2023

Advances in Structural Engineering

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Substructuring approaches possess many superiorities over traditional global approaches in damage identification because large-size global structures are replaced by small and manageable substructures. This paper proposes a substructural time series model for locating and quantifying the damage in complex systems. A substructural autoregressive moving average with exogenous inputs (ARMAX) model is established to extract the frequencies and mode shapes of substructures as indicators for damage detection. The detection of structural damage is essentially an inverse problem, and the damage in structure bears sparse properties. The inverse problem of substructural damage identification is efficiently solved via sparse regularization, and structural damage can be located and quantified through the nonzero terms in the solution vector. The accuracy of the proposed method is demonstrated by the numerical simulation of a frame structure and shaking table test of a shear building structure. As the substructural properties are more sensitive to local structural damage than the global properties, the substructural ARMAX model is quite accurate and efficient to be used in the damage identification of a complex system.

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Section: Introductionmentioning

confidence: 99%

Substructural damage identification using autoregressive moving average with exogenous inputs model and sparse regularization

Zhu

Weng

et al. 2023

Advances in Structural Engineering

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“…It can travel a long distance in the material without dissipation, so it is conducive to rapidly detecting a wide area. 25,26 When the composite bolt structure is relatively small, it will cause the superposition of incident wave and reflected wave of Lamb wave, affecting the acquisition of lamb wave energy data. The damage of composites cannot be judged according to the changes of lamb wave amplitude and other parameters.…”

Section: Introductionmentioning

confidence: 99%

Damage evolution monitoring and failure level evaluation of composite bolted joints by MXene/CNTs sensors

Zhang,

et al. 2023

Polymer Composites

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The composite bolted joints are the weak points of overall structure, lacking an effective and accurate monitoring method. In this paper, a novel MXene/CNTs film sensor with high sensitivity and stability was synthesized for the damage evolution monitoring of composite bolted joints. The correlation between the response signals of the sensors and the mechanical behaviors of the composite structures is established. Based on this research, the damage evolution monitoring and failure level evaluation of composite bolted joints were studied. The damage properties of the composite bolted joints can be reflected in real‐time by the resistance change rates of the sensors. The experimental results show that the failure level of composite bolted joints can be divided into four levels (I‐II‐III‐IV) according to the resistance change rates of sensors. Especially when the damage extended to the III stage, the surged from 0.3 to 0.75. The time point of this step signal occurred about 1 min earlier than the time point of max bearing strength, which can effectively warn the occurrence of structural failure.Highlights Research used high‐sensitivity MXene/CNTs sensor to monitor bolt structure Sensor array can be integrated with the composite bolted structure Structural damage of composite bolted joints was accurately monitored Failure level evaluation of composite bolted structure was achieved

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“…The sparse array-based imaging methods have no strict requirements on element positions, but they are usually arranged in the form of the sparse array to acquire information as much as possible from different directions and locations. It is known that the sparse array-based imaging methods include several different algorithms, such as the delay-and-sum (DAS) imaging, [8][9][10] the minimum variance distortionless response (MVDR) imaging, [11][12][13][14] the probabilistic algorithm based on signal differences, 15,16 and the model-based imaging algorithms. [17][18][19] In sparse array-based imaging, the information can be captured from multiple even all directions and the element positions in the array have no limitation.…”

Section: Introductionmentioning

confidence: 99%

Waveform covariance imaging for Lamb wave phased array

Deng

2022

Structural Health Monitoring

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The amplitude and the phase information are both important for damage localization in Lamb wave–based structural health monitoring and non-destructive testing. Most previous studies in Lamb wave imaging are only based on either the amplitude or phase of the signal at the time of flight. In this study, a post-processing technique for Lamb wave phased array imaging is proposed for isotropic plates, which simultaneously utilizes both the amplitude and phase information within a small neighborhood centered at the time of flight. In the proposed imaging algorithm, a modified virtual time reversal is firstly implemented to compensate for dispersion and the amplitude decrease caused by wave diffusion. Then the waveform covariance between any two processed wave packets, which contains information of both amplitude and phase, is used as the indicator of the presence of damage. Finally, adaptive weights based on minimum variance are introduced to weight and sum the waveform covariance for damage imaging. Based on a uniform circular array, experimental results on an aluminum plate with three defects verify that the proposed algorithm is capable of localizing multiple defects, suppressing background noise and main lobe width.

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Minimum variance Lamb wave imaging based on weighted sparse decomposition coefficients in quasi-isotropic composite laminates

Cited by 18 publications

References 27 publications

Substructural damage identification using autoregressive moving average with exogenous inputs model and sparse regularization

Substructural damage identification using autoregressive moving average with exogenous inputs model and sparse regularization

Damage evolution monitoring and failure level evaluation of composite bolted joints by MXene/CNTs sensors

Waveform covariance imaging for Lamb wave phased array

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