Influence and Compensation of Temperature Effects for Damage Detection and Localization in Aerospace Composites

Azuara, Guillermo; Barrera, E.

doi:10.3390/s20154153

Cited by 13 publications

(12 citation statements)

References 28 publications

(38 reference statements)

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“…The frequency-range analysis for the MWT was established to 50 equispaced intervals in the range of 100 kHz–600 kHz, and the time window was set to 0.16 ms duration (enough time to analyze the first arrival of the wave packets from S0 and A0 modes [ 47 , 48 ]). Moreover, final downsampling in the time-domain resulted in images of 50 × 50 pixels.…”

Section: Methodsmentioning

confidence: 99%

Damage Localization in Composite Plates Using Wavelet Transform and 2-D Convolutional Neural Networks

Azuara

Ruiz

Barrera

2021

Sensors

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Nondestructive evaluation of carbon fiber reinforced material structures has received special attention in the last decades. Usage of Ultrasonic Guided Waves (UGW), particularly Lamb waves, has become one of the most popular techniques for damage location, due to their sensitivity to defects, large range of inspection, and good propagation in several material types. However, extracting meaningful physical features from the response signals is challenging due to several factors, such as the multimodal nature of UGW, boundary conditions and the geometric shape of the structure, possible material anisotropies, and their environmental dependency. Neural networks (NN) are becoming a practical and accurate approach to analyzing the acquired data using data-driven methods. In this paper, a Convolutional-Neural-Network (CNN) is proposed to predict the distance-to-damage values from the signals corresponding to a transmitter-receiver path of transducers. The NN input is a 2D image (time-frequency) obtained as the Wavelet transform of the acquired experimental signals. The distances obtained with the NN are the input of a novel damage location algorithm which outputs a bidimensional image of the structure’s surface showing the estimated damage locations with a deviation of the actual position lower than 15 mm.

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Section: Methodsmentioning

confidence: 99%

Damage Localization in Composite Plates Using Wavelet Transform and 2-D Convolutional Neural Networks

Azuara

Ruiz

Barrera

2021

Sensors

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“…[1][2][3][4][5][6][7] Of particular interest are damage detection techniques that can be deployed with sparse UGW transducer arrays, operated either in an active mode or in a passive mode, both requiring careful decisions on signal feature extraction [8][9][10][11][12][13] and/or damage imaging algorithms. [14][15][16][17][18] Traditional UGW SHM damage imaging techniques rely on knowledge of the material properties of the test part and/ or extraction of physics-based predetermined signal features considered sensitive to damage (e.g., wave amplitude and time of flight). In fiber-reinforced composite parts, and particularly built-up components such as stiffened composite panels, such physics-based knowledge is difficult to obtain.…”

Section: Introductionmentioning

confidence: 99%

Damage imaging in skin-stringer composite aircraft panel by ultrasonic-guided waves using deep learning with convolutional neural network

Cui

Azuara

Scalea

et al. 2021

Structural Health Monitoring

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The detection and localization of structural damage in a stiffened skin-to-stringer composite panel typical of modern aircraft construction can be addressed by ultrasonic-guided wave transducer arrays. However, the geometrical and material complexities of this part make it quite difficult to utilize physics-based concepts of wave scattering. A data-driven deep learning (DL) approach based on the convolutional neural network (CNN) is used instead for this application. The DL technique automatically selects the most sensitive wave features based on the learned training data. In addition, the generalization abilities of the network allow for detection of damage that can be different from the training scenarios. This article describes a specific 1D-CNN algorithm that has been designed for this application, and it demonstrates its ability to image damage in key regions of the stiffened composite test panel, particularly the skin region, the stringer’s flange region, and the stringer’s cap region. Covering the stringer’s regions from guided wave transducers located solely on the skin is a particularly attractive feature of the proposed SHM approach for this kind of complex structure.

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“… Temperature effects compensation method, based on a state-of-the-art mathematical formulation, which fits the current data from a damaged structure to different-temperature baseline data, allowing to use pristine data recorded at different as a reference to perform baseline-comparison SHM [116].…”

Section: Discussionmentioning

confidence: 99%

“…This fact is an indicator of the presence of unconsidered parameters in the physical phenomenon. To conclude this section, the temperature effects compensation process analyzed in this thesis has been proven as an effective method for the thermoplastic matrix composite material tested, in a considerable range of aeronautical operation temperatures [116].…”

Section: Compensated-temperature Baseline Methodsmentioning

confidence: 99%

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Development of innovative techniques for lamb wave-based structural health monitoring systems in aeronautics

Pablo¹

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Influence and Compensation of Temperature Effects for Damage Detection and Localization in Aerospace Composites

Cited by 13 publications

References 28 publications

Damage Localization in Composite Plates Using Wavelet Transform and 2-D Convolutional Neural Networks

Damage Localization in Composite Plates Using Wavelet Transform and 2-D Convolutional Neural Networks

Damage imaging in skin-stringer composite aircraft panel by ultrasonic-guided waves using deep learning with convolutional neural network

Development of innovative techniques for lamb wave-based structural health monitoring systems in aeronautics

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