Probability-based diagnostic imaging with corrected weight distribution for damage detection of stiffened composite panel

Liu, Guoqiang; Wang, Binwen; Wang, Li; Yu, Yang; Wang, Xiaguang

doi:10.1177/14759217211033967

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…Many damage imaging methods can be used for defect detection in composite plates, such as the ellipse imaging method [ 9 , 10 ], delay-and-sum imaging method [ 11 , 12 ], time-reversal imaging method [ 13 , 14 ], RAPID imaging method [ 15 , 16 , 17 , 18 ], etc. The RAPID can intuitively present the location of defects by analyzing the statistical characteristics of the detection signal, and has been widely studied by researchers.…”

Section: Introductionmentioning

confidence: 99%

An Improved RAPID Imaging Method of Defects in Composite Plate Based on Feature Identification by Machine Learning

Deng

Zhang

et al. 2022

Sensors

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The RAPID (reconstruction algorithm for probabilistic inspection of defect) method based on Lamb wave detection is an effective method to give the position information of a defect in composite plate. In this paper, an improved RAPID imaging method based on machine learning (ML) is proposed to precisely visualize the location and features of defects in composite plate. First, the specific feature information of the defect, such as type, size and direction, can be identified by analyzing the detection signals through multiple machine learning models. Then, according to the obtained defect features, the scaling parameter β of the RAPID method which controls the size of the elliptical area is revised, and weights are set to the important detection paths which are related to defect features to realize precise defect imaging. The simulation results show that the proposed method can intuitively characterize the location and related feature information of the defect, and effectively improve the accuracy of defect imaging.

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Section: Introductionmentioning

confidence: 99%

An Improved RAPID Imaging Method of Defects in Composite Plate Based on Feature Identification by Machine Learning

Deng

Zhang

et al. 2022

Sensors

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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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“…Liu et al [22] developed a PDI algorithm that localizes the damage within an aircraft pallet. Liu et al [23] developed an improved PDI algorithm to localize artificial and impact damage on a stiffed composite panel.…”

Section: Introductionmentioning

confidence: 99%

“…In [21], an average over all damage positions to quantify the localization accuracy was also calculated. In [23], the localization error varies between (25-50) mm for the impact damage and (4-25) mm for the artificial damage. They emphasize on the development of novel localization techniques, while statistical quantification of localization accuracy has not been focused.…”

Section: Introductionmentioning

confidence: 99%

Determination of Detection Probability and Localization Accuracy for a Guided Wave-Based Structural Health Monitoring System on a Composite Structure

2021

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The capabilities of detection and localization of damage in a structure, using a guided wave-based structural health monitoring (GWSHM) system, depend on the damage location and the chosen sensor array setup. This paper presents a novel approach to assess the reliability of an SHM system enabling to quantify localization accuracy. A two-step technique is developed to combine multiple paths to generate one probability of detection (POD) curve that provides information regarding the detection capability of an SHM system at a defined damage position. Moreover, a new method is presented to analyze localization accuracy. Established probability-based diagnostic imaging using a signal correlation algorithm is used to determine the damage location. The resultant output of the localization accuracy analysis is the smallest damage size at which a defined accuracy level can be reached at a determined location. The proposed methods for determination of detection probability and localization accuracy are applied to a plate-like CFRP structure with an omega stringer with artificial damage of different sizes at different locations. The results show that the location of the damage influences the sensitivity of detection and localization accuracy for the used detection and localization methods. Localization accuracy is enhanced as it becomes closer to the array’s center, but its detection sensitivity deteriorates.

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Probability-based diagnostic imaging with corrected weight distribution for damage detection of stiffened composite panel

Cited by 12 publications

References 24 publications

An Improved RAPID Imaging Method of Defects in Composite Plate Based on Feature Identification by Machine Learning

An Improved RAPID Imaging Method of Defects in Composite Plate Based on Feature Identification by Machine Learning

Waveform covariance imaging for Lamb wave phased array

Determination of Detection Probability and Localization Accuracy for a Guided Wave-Based Structural Health Monitoring System on a Composite Structure

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