Defect Imaging Enhancement through Optimized Shape Factors of the RAPID Algorithm Based on Guided Wave Beam Pattern Analysis

Lee, Yonghee; Cho, Younho

doi:10.3390/s21124029

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…In addition, a Gaussian distribution is employed to enhance the trajectory coverage area and decrease the localization error. Furthermore, the probability imaging algorithm grids the monitoring area of the sensor network into uniform pixel points [32]. The damage probability of each pixel point in the elliptical influence area of each sensing path is superimposed [31].…”

Section: Damage Detection Methodsmentioning

confidence: 99%

Lamb Wave-Based Damage Localization and Quantification in Composites Using Probabilistic Imaging Algorithm and Statistical Method

Guo

Zeng

Liu

et al. 2022

Sensors

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Quantitatively and accurately monitoring the damage to composites is critical for estimating the remaining life of structures and determining whether maintenance is essential. This paper proposed an active sensing method for damage localization and quantification in composite plates. The probabilistic imaging algorithm and the statistical method were introduced to reduce the impact of composite anisotropy on the accuracy of damage detection. The matching pursuit decomposition (MPD) algorithm was utilized to extract the precise TOF for damage detection. The damage localization was realized by comprehensively evaluating the damage probability evaluation results of all sensing paths in the monitoring area. Meanwhile, the scattering source was recognized on the elliptical trajectory obtained through the TOF of each sensing path to estimate the damage size. Damage size was characterized by the Gaussian kernel probability density distribution of scattering sources. The algorithm was validated by through-thickness hole damages of various locations and sizes in composite plates. The experimental results demonstrated that the localization and quantification absolute error are within 11 mm and 2.2 mm, respectively, with a sensor spacing of 100 mm. The algorithm proposed in this paper can accurately locate and quantify damage in composite plate-like structures.

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Section: Damage Detection Methodsmentioning

confidence: 99%

Lamb Wave-Based Damage Localization and Quantification in Composites Using Probabilistic Imaging Algorithm and Statistical Method

Guo

Zeng

Liu

et al. 2022

Sensors

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“…Even though it was not possible to obtain detailed information on different modes, good results were achieved for damage location and defect size characterisation [60]. Furthermore, a reconstruction algorithm for probabilistic inspection of damage (RAPID) was used for tomography [61], with a more accurate quantitative visualisation obtained using the dominant mode, identified through frequency shifting and short-time Fourier transform [39]. Images constructed from the correlation coefficients between the scattering signal and the atoms of the dictionary using a weighted sparse reconstruction-based anomaly imaging method yield accurate weights [62].…”

Section: Ianni Et Al Accomplished the Minimisation Of The Number Of S...mentioning

confidence: 99%

A Review of Signal Processing Techniques for Ultrasonic Guided Wave Testing

Diogo

Moreira²,

Gouveia³

et al. 2022

Metals

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Ultrasonic guided wave testing (UGWT) is a non-destructive testing (NDT) technique commonly used in structural health monitoring to perform wide-range inspection from a single point, thus reducing the time and effort required for NDT. However, the multi-modal and dispersive nature of guided waves makes the extraction of essential information that leads to defect detection an extremely challenging task. The purpose of this article is to give an overview of signal processing techniques used for filtering signals, isolating modes and identifying and localising defects in UGWT. The techniques are summarised and grouped according to the geometry of the studied structures. Although the reviewed techniques have led to satisfactory results, the identification of defects through signal processing remains challenging with space for improvement, particularly by combining signal processing techniques and integrating machine learning algorithms.

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“…In the conventional RAPID algorithm, the damage index is determined using the signal difference coefficient, which is based on Pearson's correlation coefficient between signals acquired with and without the presence of a defect. Therefore, the conventional RAPID method requires accurate baseline data, and its efficacy can be affected by environmental factors [29][30][31]. The time reversal method (TRM) based on UGWs is a candidate for baseline-free damage imaging.…”

Section: Introductionmentioning

confidence: 99%

Baseline-Free Damage Imaging of Composite Lap Joint via Parallel Array of Piezoelectric Sensors

Barzegar,

Ribeiro,

Pasadas

et al. 2023

Sensors

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This paper presents a baseline-free damage imaging technique using a parallel array of piezoelectric sensors and a control board that facilitates custom combinations of sensor selection. This technique incorporates an imaging algorithm that uses parallel beams for generation and reception of ultrasonic guided waves in a pitch–catch configuration. A baseline-free reconstruction algorithm for probabilistic inspection of defects (RAPID) algorithm is adopted. The proposed RAPID method replaces the conventional approach of using signal difference coefficients with the maximum signal envelope as a damage index, ensuring independence from baseline data. Additionally, conversely to the conventional RAPID algorithm which uses all possible sensor combinations, an innovative selection of combinations is proposed to mitigate attenuation effects. The proposed method is designed for the inspection of lap joints. Experimental measurements were carried out on a composite lap joint, which featured two dissimilar-sized disbonds positioned at the lap joint’s borderline. A 2D correlation coefficient was used to quantitatively determine the similarity between the obtained images and a reference image with correct defect shapes and locations. The results demonstrate the effectiveness of the proposed damage imaging method in detecting both defects. Additionally, parametric studies were conducted to illustrate how various parameters influence the accuracy of the obtained imaging results.

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Defect Imaging Enhancement through Optimized Shape Factors of the RAPID Algorithm Based on Guided Wave Beam Pattern Analysis

Cited by 5 publications

References 27 publications

Lamb Wave-Based Damage Localization and Quantification in Composites Using Probabilistic Imaging Algorithm and Statistical Method

Lamb Wave-Based Damage Localization and Quantification in Composites Using Probabilistic Imaging Algorithm and Statistical Method

A Review of Signal Processing Techniques for Ultrasonic Guided Wave Testing

Baseline-Free Damage Imaging of Composite Lap Joint via Parallel Array of Piezoelectric Sensors

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