In situ damage classification for composite laminates using Gaussian discriminant analysis

Larrosa, Cecilia C.; Lonkar, Kuldeep; Chang, Fu‐Kuo

doi:10.1177/1475921713517288

Cited by 61 publications

(42 citation statements)

References 24 publications

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“…Worden & Mason 5 has used kernel density estimation method to estimate the density of the damage-sensitive features and then used it to estimate the Bayesian posterior probability of each class to make decision about the health of the structure. Larossa et al 6 have used time-domain analysis and short-time Fourier transform combined with Gaussian discriminant analysis to characterize damage in the composite. Cremona 7 have used supervised learning methods like Bayesian decision tree and Support-vector machine to discriminate structural features.…”

Section: Relevant Literature On Discriminant Analysis In Shmmentioning

confidence: 99%

Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves

et al. 2016

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In this study we propose a regularized linear discriminant analysis approach for damage detection which does not require an intermediate feature extraction step and therefore more efficient in handling data with highdimensionality. A robust discriminant model is obtained by shrinking of the covariance matrix to a diagonal matrix and thresholding redundant predictors without hurting the predictive power of the model. The shrinking and threshold parameters of the discriminant function (decision boundary) are estimated to minimize the classification error. Furthermore, it is shown how the damage classification achieved by the proposed method can be extended to multiple sensors by following a Bayesian decision-fusion formulation. The detection probability of each sensor is used as a prior condition to estimate the posterior detection probability of the entire network and the posterior detection probability is used as a quantitative basis to make the final decision about the damage.

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Section: Relevant Literature On Discriminant Analysis In Shmmentioning

confidence: 99%

Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves

et al. 2016

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“…(d) Electromagnetic testing: In NDE electromagnetic testing, composite laminates undergo a process in which the internal anomalies are inspected in terms of electromagnetic responses utilizing electric currents or magnetic fields induced. As shown in Figure 6, radiography, computed tomography, terahertz (THz) spectroscopy, and the electrical resistance change-based method may be included in electromagnetic testing [107][108][109][110][111][112][113][114][115][116][117][118][119][120]. In addition, thermography, as explained above, is a part of electromagnetic testing.…”

Section: Nde Techniquesmentioning

confidence: 99%

“…With respect to delamination detection, estimating the damage type of matrix cracking and delamination developed under fatigue loads, using the data of the PZT transducer smart layer so as to sense Lamb waves attached to composite laminates of different layups to account for the ply orientation influence and X-ray images enhanced by a dye penetrant subjected to signal processing, have been investigated [107]. Layup configuration being capable of inserting an influence on output signals, nevertheless, TOF and the signal's amplitude can be utilized for damage classification regardless of the layup.…”

Section: Radiographymentioning

confidence: 99%

A Survey of Scrutinizing Delaminated Composites via Various Categories of Sensing Apparatus

Askaripour

Żak

2019

J. Compos. Sci.

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Scrutinizing delaminated composites is extensively conducted based on measurements of various types of nondestructive sensing apparatus, classified herein as systems of thermographic inspection, piezoelectric inspection, optical interferometric inspection, electromagnetic testing, ultrasonic testing, and visual testing. The development of the subject has been reviewed during 2000–2017. Not only does the substantial corresponding literature strongly indicates numerous increasing industrial demand for composites, including carbon fiber-reinforced polymer, glass fiber-reinforced polymer, and fiber-reinforced metal laminate, but also the significant potential of composites to delaminate when subjected particularly to impact, machining operation or manufacturing imperfections. Conducting a study on the corresponding literature, the subject of delamination inspection within composites is found to be a dynamic, mature field, while lacking a thorough literature review. Therefore, the present study addresses the mentioned gap regarding various sensation systems utilized so as to detect internal anomalies in composites for the review arrangement. To that end, the literature inclusion in terms of citation times is electronically adopted associated with an appropriate combination of keywords. The study may be considered as a comprehensive, up-to-date review covering all delamination inspection techniques founded on sensation systems, thus benefiting the readers with information in an organized configuration.

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“…Delamination is a representative type of damage in composite laminates, most frequently occurring as interlaminar separation of adjoining plies and imperfect bonding (Pines and Purekar, 2010;_ Zak et al, 2000), which severely jeopardizes the safety of structures (Larrosa et al, 2014). Identification of delamination in composite laminates is a research topic of significant importance.…”

Section: Introductionmentioning

confidence: 99%

Non-baseline identification of delamination in plates using wavelet-aided fractal analysis of two-dimensional mode shapes

Bai

Radzieński

Cao

et al. 2014

Journal of Intelligent Material Systems and Structures

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Delamination is a typical form of damage in composite plates. Identification of delamination in plates has been a focus of increasing research interest in relatively recent years. This study develops a new approach, termed a scale waveform dimension analysis of two-dimensional mode shapes, to identify delamination in composite plates. The scale waveform dimension analysis comprises two components: decomposition of a mode shape into scale mode shapes and waveform dimension analysis of scale mode shapes. The first component acts as a splitter that broadly splits a noisy mode shape into three sets of trend-, noise-, and damage-scale mode shapes, from which the damage scale mode shapes can be selected for use in damage characterization; the second component functions as a detector to detect abnormalities of damage scale mode shapes, to indicate the presence and location of delamination. The efficacy of the method is numerically studied and experimentally examined using composite plates containing small delamination. The results show that the proposed method can identify delamination with great accuracy in noisy conditions, needing no intact baseline mode shapes nor any prior knowledge of either the material properties or boundary conditions of the plate being inspected.

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In situ damage classification for composite laminates using Gaussian discriminant analysis

Cited by 61 publications

References 24 publications

Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves

Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves

A Survey of Scrutinizing Delaminated Composites via Various Categories of Sensing Apparatus

Non-baseline identification of delamination in plates using wavelet-aided fractal analysis of two-dimensional mode shapes

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