Multiple damage detection in laminated composite beams by data fusion of Teager energy operator-wavelet transform mode shapes

Sha, Ganggang; Radzieński, Maciej; Soman, Rohan; Cao, Maosen; Ostachowicz, Wiesław; Xu, Weiya

doi:10.1016/j.compstruct.2019.111798

Cited by 45 publications

(32 citation statements)

References 36 publications

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“…To overcome the drawbacks of traditional NDT methods, structural health monitoring (SHM) techniques were developed to assess integrity, reliability, and safety of structures in-time, in-situ, and continuously. 7,8 SHM techniques used for composite laminates include vibration-based, [9][10][11][12] optical fiber gratings, 8,13 guided-wave based techniques, [14][15][16][17] acoustic emission, 18,19 and impedance-based techniques. 20,21 Among them, vibration-based monitoring which uses changes in vibration parameters of the structure has a long history and it is regarded as one of the most important SHM techniques.…”

Section: Introductionmentioning

confidence: 99%

A comparison of machine learning algorithms for assessment of delamination in fiber-reinforced polymer composite beams

Wang

Ramakrishnan

et al. 2020

Structural Health Monitoring

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Structural health monitoring techniques based on vibration parameters have been used to assess the internal delamination damage of fiber-reinforced polymer composites. Recently, machine learning algorithms have been adopted to solve the inverse problem of predicting delamination parameters of the delamination from natural frequency shifts. In this article, a delamination detection methodology is proposed based on the changes in multiple modes of frequencies to assess the interface, location, and size of delamination in fiber-reinforced polymer composites. Three types of machine learning algorithms including back propagation neural network, extreme learning machine, and support vector machine algorithm were adopted as inverse algorithms for assessment of the delamination parameters, with a special focus on the interface prediction. A theoretical model of fiber-reinforced polymer beam with delamination under vibration was constructed to learn how the frequencies are affected by the delaminations (“forward problem”) and to generate a database of “frequency shifts versus delamination parameters” to be used in machine learning algorithms for delamination prediction (“inverse problem”). Multiple carbon/epoxy fiber-reinforced polymer beam specimens were manufactured and measured by a laser scanning Doppler vibrometer to extract the modal frequencies. Numerical and experimental verification results have shown that support vector machine has the best prediction performance among the three machine learning algorithms, with high prediction accuracy and only requiring a small number of samples. For predicting the interface of delamination which is a discrete variable, support vector machine classification has observed better prediction accuracy and requiring less running time than regression. This study is one of the first to prove the applicability of support vector machine for structural health monitoring of delamination damage in fiber-reinforced polymer composites and has the potential to improve the prediction capability of machine learning algorithms. Another significant outcome of the study is that the interface of delamination has been predicted accurately with support vector machine.

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Section: Introductionmentioning

confidence: 99%

A comparison of machine learning algorithms for assessment of delamination in fiber-reinforced polymer composite beams

Wang

Ramakrishnan

et al. 2020

Structural Health Monitoring

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“…These metrics include average gradient, entropy, edge intensity, standard deviation, local contrast, PSNR, and the Xydeas and Petrovic [35]. [20], [52]- [70].…”

Section: Fusion Quality Evaluation Metricsmentioning

confidence: 99%

A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

et al. 2021

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The track of medical imaging has witnessed several advancements in the last years. Several medical imaging modalities have appeared in the last decades including X-ray, Computed Tomography (CT), Magnetic Resonance (MR), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT) and ultrasound imaging. Generally, medical images are used for the diagnosis purpose. Each type of acquired images has some merits and limitations. To maximize medical images utilization for the purpose of diagnosis, medical imaging fusion trend has appeared as a hot research field. Different medical imaging modalities are fused to obtain new images with complementary information. This paper presents a survey study of medical imaging modalities and their characteristics. In addition, different medical image fusion approaches and their appropriate quality metrics are presented. The main aim of this comprehensive survey analysis is to contribute in the advancement of medical image approaches that can help for better diagnosis of different diseases.INDEX TERMS Medical imaging, fusion process, imaging modalities, dual-tree complex wavelet transform, curvelet transform, discrete wavelet transform, principal component analysis.

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“…Several methods for damage detection based on modal parameters have been reported in the literature. In different works, natural frequencies, curvature mode shapes, and modal analysis have been applied to damage detection in one-dimensional beams [ 2 , 3 , 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Experimental Detection and Measurement of Crack-Type Damage Features in Composite Thin-Wall Beams Using Modal Analysis

Pacheco-Chérrez

Cárdenas

Probst

2021

Sensors

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An experimental proof-of-concept for damage detection in composite beams using modal analysis has been conducted. The purpose was to demonstrate that damage features can be detected, located, and measured on the surface of a relatively complex thin-wall beam made from composite material. (1) Background: previous work has been limited to the study of simple geometries and materials. (2) Methods: damage detection in the work is based on the accurate measurement of mode shapes and an appropriate design of the detection mesh. Both a method requiring information about the healthy structure and a baseline-free method have been implemented. (3) Results: short crack-type damage features, both longitudinal and transverse, were detected reliably, and the true length of the crack can be estimated from the damage signal. Simultaneous detection of two cracks on the same sample is also possible. (4) This work demonstrates the feasibility of automated damage detection in composite beams using sensor arrays.

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Multiple damage detection in laminated composite beams by data fusion of Teager energy operator-wavelet transform mode shapes

Cited by 45 publications

References 36 publications

A comparison of machine learning algorithms for assessment of delamination in fiber-reinforced polymer composite beams

A comparison of machine learning algorithms for assessment of delamination in fiber-reinforced polymer composite beams

A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

Experimental Detection and Measurement of Crack-Type Damage Features in Composite Thin-Wall Beams Using Modal Analysis

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