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

Guo, Jiahui; Zeng, Xianlin; Liu, Qijian; Qing, Xinlin

doi:10.3390/s22134810

Cited by 13 publications

(8 citation statements)

References 36 publications

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“…Localization of the damage needs to be done from these values of time-of-flight. There are algorithms reported 13,20 for this purpose when the structure is excited by S o mode, like forming the ellipses of possible damage location from various sensor locations and extracting the location using suitable algorithms etc. A similar algorithm could be thought of for mode converted S o waves when excited A o mode.…”

Section: Damage Detection Using Mode Converted Guided Wavementioning

confidence: 99%

“…With advances in neurosciences and high-capability computing devices, recent research is focused on application of machine learning (ML) algorithms based on Artificial Neural Networks (ANN) for guided wave damage identification, localization and qualification including an assessment on the probability of occurrence of damage in metallic and composite structural members. [14][15][16][17][18][19] Jiahui et al 20 utilized probabilistic imaging algorithm and statistical method to reduce the impact of composite anisotropy in Lamb wave-based damage localization and quantification in composite plate like structures. The algorithm was validated by experiments and results indicate an accurate prediction of the damage localization and quantification with an absolute error within 11 mm and 2.2 mm respectively for a sensor spacing of 100 mm.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Quantification of Asymmetrically Located Structural Damages by Mode Converted Guided Waves Using Piezo Electric Elements

Patnaik¹,

Renji²,

Gopal³

2023

The International Journal of Acoustics and Vibration

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Though damage identification using guided waves generated using ultrasonics is well proven, its usage for structural health monitoring poses difficulties. Piezo electric actuation and sensing overcomes this difficulty to some extent. In this work, usage of such guided waves for damage identification is investigated. Piezo electric wafer transducers are used for generating and sensing the guided waves. Presence of multiple modes and comparatively higher speeds of the guided waves throw up difficulties in damage identification. It is shown here that this problem can be addressed by considering different sensor location with respect to the damage with suitable interpretation of the results. Usage of fundamental antisymmetric (Ao) mode is found to be more suitable in localizing the damage compared to the fundamental symmetric (So) mode. Asymmetrically located damage causes mode conversion. It is demonstrated in this work that the mode converted guided wave (So) could be advantageously used for identification, localization, and quantification of the damage. Damage identification and localization schemes are evolved based on the location of the sensors with respect to the damage. It is shown that the reduction in the magnitude of the mode converted wave can be utilized for assessing the depth of the damage. 3D finite element based numerical models incorporating a PZT sensor are developed and validated with experimental results in terms of the characteristics of the waves, mode conversion due to damage and influence of the defect size on the received signals which are necessary for quantification of the damage.

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Section: Damage Detection Using Mode Converted Guided Wavementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection and Quantification of Asymmetrically Located Structural Damages by Mode Converted Guided Waves Using Piezo Electric Elements

Patnaik¹,

Renji²,

Gopal³

2023

The International Journal of Acoustics and Vibration

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“…This method is feasible on quasi-isotropic materials and, as in the previous case, the interpretation of the signals and the automatisation of this algorithm can be complex. Guo et al [16] introduced a probabilistic imaging algorithm and a statistical method for detect through-thickness hole position and size in a composite structure. The probabilistic imaging algorithm and the statistical method were introduced to reduce the impact of composite anisotropy on the accuracy of damage detection.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a probabilistic multistage algorithm for damage localization in piezo-monitored structures

Luca

Perfetto

Polverino

et al. 2023

Smart Mater. Struct.

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All structures during operating life can be affected by faults induced by accidental events and operational conditions. Structural health monitoring (SHM) systems can provide quasi-real-time diagnosis of the structure, thus enabling the condition-based maintenance approach. By means of piezoelectric transducers (PZTs) and ultrasonic guided waves (UGW), the structural integrity can be easily interrogated, even though laborious post-processing techniques are required to correctly interpret sensed data. This work aims to devise a new automatic diagnosis framework based on the propagation of UGW for thin-walled structures fault detection and localisation. Specifically, a fully automated damage identification algorithm was developed through a numerical dataset obtained by Finite Element simulations, and then validated experimentally. The case of study consisted of a square-shaped aluminium plate equipped with a five PZTs network. Five different damage positions and three different damage sizes were considered. The originality of the proposed algorithm lies in the data processing methodology as well as in its capability to detect damages located inside and outside the sensors network, even close to the panel edges. Algorithm provides, in less than 15 s, indications on the possible damage location and related probability position with a reduced dispersion with respect to other algorithms proposed in literature. A clear image is created displaying the damage position map. The visualization of the field probability map on the surface of the monitored part allows successful damage imaging and would enable operators to address more efficiently the inspection procedures only in the highlighted areas, reducing maintenance and repair expenses

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“…Cao et al 45 proposed a probability weighted four-point arc algorithm for damage imaging. Guo et al 46 used matching pursuit decomposition algorithm to extract damage feature (time-of-flight) and a Gaussian kernel probability density distribution was adopted to characterize the damage.…”

Section: Introductionmentioning

confidence: 99%

A time-reversal assisted probabilistic fusion strategy for damage localization in plate-like structures using Lamb waves

Zhang

Zhou

et al. 2023

Structural Health Monitoring

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Precise damage identification and quantification is of great significance to ensure structural performance and provide early-warning for safety maintenance. In this research, a time-reversal assisted probabilistic strategy is developed to accurately localize damage via baseline-free manner in plate-like structures by fusing damage prediction data obtained from the elliptical trajectory location method (ETLM) and reconstruction algorithm for probabilistic inspection of defects (RAPID) algorithm. Damage features, including time difference for ETLM and correlation coefficient index (CCI) for RAPID algorithm, are extracted from the time-reversal focusing signal using the Hilbert transform (HT). To make full use of the damage-related information contained in the time-reversal focusing signals and improve the localizing accuracy, a decision-level data fusion based on Bayesian inference is proposed to fuse damage features and reconstruct damage information. A series of numerical and experimental studies, including different configurations of damage cases and transducer distributions, were conducted to investigate the performances of the proposed time-reversal assisted probabilistic method on damage localization. Results shows that the proposed method could successfully achieve accurate damage localization via baseline-free manner and significantly reduce the damage artifacts compared to traditional methods.

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

Cited by 13 publications

References 36 publications

Detection and Quantification of Asymmetrically Located Structural Damages by Mode Converted Guided Waves Using Piezo Electric Elements

Detection and Quantification of Asymmetrically Located Structural Damages by Mode Converted Guided Waves Using Piezo Electric Elements

Development and validation of a probabilistic multistage algorithm for damage localization in piezo-monitored structures

A time-reversal assisted probabilistic fusion strategy for damage localization in plate-like structures using Lamb waves

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