Sparse sensing detection of impact-induced delaminations in composite laminates

Gaudenzi, Paolo; Nardi, Davide; Chiappetta, Ilaria; Atek, Sofiane; Lampani, Luca; Pasquali, Michele; Sarasini, Fabrizio; Tirillò, Jacopo; Valente, Teodoro

doi:10.1016/j.compstruct.2015.08.052

Cited by 29 publications

(11 citation statements)

References 30 publications

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“…The curvature of the mode shapes is obtained by implementing a finite element model of the unidirectional Carbon/Epoxy plate. The material mechanical properties adopted in the numerical model and its description can be found in [10]. The proposed work is limited to the first three resonance modes.…”

Section: Piezo Devices Placement and Data Acquisitionmentioning

confidence: 99%

“…The latter, which is employed in this work, consists of fitting an AR model to time-history data from the undamaged and damaged structural configurations and hence use them as classifier among the considered damage scenarios. The proposed damage sensitive feature is selected to overcome the limitations of other more common procedure based, for example, on the assessment of changes in the frequency response functions of the specimens [10].…”

Section: Data Fitting Via Auto-regressive Modelmentioning

confidence: 99%

“…The class-scatter matrix S c expresses the scattering of the samples belonging to the same class (configuration). The within-class scatter matrix S w is the p-class probability weighted sum of previously defined class scatter matrix and the between-class scatter matrix S b expresses the scattering of mean vectors of classes with respect to the mean vector of the mean vectors [10]. From the product between the inverse matrix of S w and the matrix S b it is possible to obtain a m Â m matrix (where m is the number of the adopted AR parameters) named V representing the ratio of the scattering between different classes and the scattering within the same class.…”

Section: Pattern Recognition Proceduresmentioning

confidence: 99%

“…This SHM routine is based on previous damage pattern recognition studies performed on composite beam [9] and plates [10] via wavelet-based approach. The novelty here reported is the use of AR parameters as damage sensitive features.…”

Section: Introductionmentioning

confidence: 99%

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Detection of low-velocity impact-induced delaminations in composite laminates using Auto-Regressive models

Nardi

Lampani

Pasquali

et al. 2016

Composite Structures

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In this paper, the detection of delaminations in carbon-fiber-reinforced-plastic (CFRP) laminate plates induced by low-velocity impacts (LVI) is investigated by means of Auto-Regressive (AR) models obtained from the time histories of the acquired responses of the composite specimens. A couple of piezoelectric patches for actuation and sensing purposes are employed. The proposed structural health monitoring (SHM) routine begins with the selection of the suitable locations of the piezoelectric transducers via the numerical analysis of the curvature mode shapes of the CFRP plates. The normalized data recorded for the undamaged plate configuration are then analyzed to obtain the most suitable AR model using five techniques based on the Akaike Information Criterion (AIC), the Akaike Final Prediction Error (FPE), the Partial Autocorrelation Function (PAF), the Root Mean Squared (RMS) of the AR residuals for different order p, and the Singular Value Decomposition (SVD). Linear Discriminant Analysis (LDA) is then applied on the AR model parameters to enhance the performance of the proposed delamination identification routine. Results show the effectiveness of the developed procedure when a reduced number of sensors is available.

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Section: Piezo Devices Placement and Data Acquisitionmentioning

confidence: 99%

Section: Data Fitting Via Auto-regressive Modelmentioning

confidence: 99%

Section: Pattern Recognition Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of low-velocity impact-induced delaminations in composite laminates using Auto-Regressive models

Nardi

Lampani

Pasquali

et al. 2016

Composite Structures

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“…The classification methods used were linear discriminant analysis, support vector machines, random forests and adaptive resonance theory-Kohonen neural network. In [19], the linear discriminant analysis method was adopted as a classifier for the damage detection in composite structures with the aid of a wavelet packet transformbased algorithm. A naïve Bayes classifier was used in [20] and shown to be one of the most promising classification approaches for damage detection.…”

Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring for Condition Assessment Using Efficient Supervised Learning Techniques

Entezami

Shariatmadar

Mariani

2019

The 6th International Electronic Conference on Sensors and Applications

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Pattern recognition can be adopted for structural health monitoring (SHM) based on statistical characteristics extracted from raw vibration data. Structural condition assessment is an important step of SHM, since changes in the relevant properties may adversely affect the behavior of any structure. It looks therefore necessary to adopt efficient and robust approaches for the classification of different structural conditions using features extracted from the said raw data. To achieve this goal, it is essential to correctly distinguish the undamaged and damage states of the structure; the aim of this work is to present and compare classification methods using feature selection techniques to classify the structural conditions. All of the utilized classifiers need a training set pertinent to the undamaged/damaged conditions of the structure, as well as relevant class labels to be adopted in a supervised learning strategy. The performance and accuracy of the considered classification methods are assessed through a numerical benchmark concrete beam.

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Analysis of Damage in Hybrid Composites Subjected to Ballistic Impacts: An Integrated Non‐Destructive Approach

Сфарра

López

Sarasini

et al. 2017

Handbook of Composites From Renewable Materials

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Sparse sensing detection of impact-induced delaminations in composite laminates

Cited by 29 publications

References 30 publications

Detection of low-velocity impact-induced delaminations in composite laminates using Auto-Regressive models

Detection of low-velocity impact-induced delaminations in composite laminates using Auto-Regressive models

Structural Health Monitoring for Condition Assessment Using Efficient Supervised Learning Techniques

Analysis of Damage in Hybrid Composites Subjected to Ballistic Impacts: An Integrated Non‐Destructive Approach

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