Deep learning approach for delamination identification using animation of Lamb waves

Ullah, Saeed; Ijjeh, Abdalraheem; Kudela, Paweł

doi:10.1016/j.engappai.2022.105520

Cited by 16 publications

(5 citation statements)

References 44 publications

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“…Although, according to Table 2, the current results are not as good as compared to our previous paper [11], the advantage of the proposed method is that it can be easily extended to cases in which only a limited number of signals are available in comparison to full wavefield data. This is extremely important for practical applications in structural health monitoring where only the signals at sensor locations are available.…”

Section: Delamination Identification Resultscontrasting

confidence: 59%

“…It should be noted that despite the low IoU values in certain cases, the identification algorithm performed remarkably well, because the delamination was localised accurately for each scenario. The delamination identification results in terms of the IoU values are gathered in Table 2, wherein we also find the results from [11], which have been added for comparison.…”

Section: Delamination Identification Resultsmentioning

confidence: 99%

“…The method presented in [11] is completely different from the one presented here, but it relies on the same dataset. However, the frame size was larger, namely (512 × 512) pixels versus (256 × 256) pixels here, thus giving a better resolution of the damage identification.…”

Section: Delamination Identification Resultsmentioning

confidence: 99%

“…In our previous works [8,11], full wavefield data were directly processed to obtain damage maps. Here, full wavefield data are used to train a deep learning (DL) model for predicting guided wave interactions with defects.…”

Section: Introductionmentioning

confidence: 99%

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Simulation of Full Wavefield Data with Deep Learning Approach for Delamination Identification

Ullah,

Kudela,

Ijjeh

et al. 2024

Applied Sciences

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In this work, a novel approach of guided wave-based damage identification in composite laminates is proposed. The novelty of this research lies in the implementation of ConvLSTM-based autoencoders for the generation of full wavefield data of propagating guided waves in composite structures. The developed surrogate deep learning model takes as input full wavefield frames of propagating waves in a healthy plate, along with a binary image representing delamination, and predicts the frames of propagating waves in a plate, which contains single delamination. The evaluation of the surrogate model is ultrafast (less than 1 s). Therefore, unlike traditional forward solvers, the surrogate model can be employed efficiently in the inverse framework of damage identification. In this work, particle swarm optimisation is applied as a suitable tool to this end. The proposed method was tested on a synthetic dataset, thus showing that it is capable of estimating the delamination location and size with good accuracy. The test involved full wavefield data in the objective function of the inverse method, but it should be underlined as well that partial data with measurements can be implemented. This is extremely important for practical applications in structural health monitoring where only signals at a finite number of locations are available.

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Section: Delamination Identification Resultscontrasting

confidence: 59%

Section: Delamination Identification Resultsmentioning

confidence: 99%

Section: Delamination Identification Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simulation of Full Wavefield Data with Deep Learning Approach for Delamination Identification

Ullah,

Kudela,

Ijjeh

et al. 2024

Applied Sciences

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“…In [3], [17] and [18] authors addressed the problem of automatic delamination detection in CFRP plates by means of FCNs architectures, but the effect of spatial downsampling was not examined in these works. Instead, super-resolution of full wavefield images merely relying on deep learning strategies was performed in [7].…”

Section: Related Workmentioning

confidence: 99%

Enhanced Global Convolution Networks for Defect Detection in Full Wavefield Imaging

Donati,

Zonzini,

De Marchi

2024

J. Phys.: Conf. Ser.

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Non-Destructive Evaluation (NDE) techniques based on ultrasonic guided waves can provide relevant information about the health status of an inspected structure. The analysis of the wavefields recorded through Scanning Laser Doppler Vibrometers (SLDVs) has been used successfully in NDE systems to detect damages in planar isotropic materials, such as interlaminar fractures in composites. However, the high spatial point resolution required to ensure an accurate localization and quantification of defects typically implies a time-consuming acquisition process, which limits the applicability of such approaches. In this work, we exploit the potential of convolutional neural networks (CNNs) to perform segmentation on full wavefield images, obtained by reconstruction from a low resolution grid having a spatial sampling rate below the Nyquist frequency, for the purpose of detecting and localising delaminations in carbon fibre reinforced plastic (CFRP) plates. In particular, we trained an improved version of Global Convolutional Networks (GCN) using a public dataset containing 475 simulated cases of full wavefield Lamb waves propagation in a CFRP plate, generated by an actuator with a carrier frequency of 50 kHz. We adopted channel and spatial attention mechanisms to improve the accuracy of the networks and applied our method on (i) the resulting image given by the root mean square value (RMS) in time for each spatial position and on (ii) the 3-dimensional animation representing the full wavefield propagation. Our networks show the ability to accurately locate target damages with a spatial resolution 8 times higher than the dimension of the adopted sampling grid, achieving an Intersection over Union score equal to 0.78 with a number of scanning point more than 60 times lower than the number of pixels in the output segmentation mask.

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An explainable artificial intelligence‐based approach for reliable damage detection in polymer composite structures using deep learning

Azad,

Kim

2024

Polymer Composites

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Artificial intelligence (AI) techniques are increasingly used for structural health monitoring (SHM) of polymer composite structures. However, to be confident in the trustworthiness of AI models, the models must be reliable, interpretable, and explainable. The use of explainable artificial intelligence (XAI) is critical to ensure that the AI model is transparent in the decision‐making process and that the predictions it provides can be trusted and understood by users. However, existing SHM methods for polymer composite structures lack explainability and transparency, and therefore reliable damage detection. Therefore, an interpretable deep learning model based on an explainable vision transformer (X‐ViT) is proposed for the SHM of composites, leading to improved repair planning, maintenance, and performance. The proposed approach has been validated on carbon fiber reinforced polymers (CFRP) composites with multiple health states. The X‐ViT model exhibited better damage detection performance compared to existing popular methods. Moreover, the X‐ViT approach effectively highlighted the area of interest related to the prediction of each health condition in composites through the patch attention aggregation process, emphasizing their influence on the decision‐making process. Consequently, integrating the ViT‐based explainable deep‐learning model into the SHM of polymer composites provided improved diagnostics along with increased transparency and reliability.Highlights Autonomous damage detection of polymer composites using vision transformer based deep learning model. Explainable artificial intelligence by highlighting region of interest using patch attention. Comparison with the existing state of the art structural health monitoring methods.

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Deep learning approach for delamination identification using animation of Lamb waves

Cited by 16 publications

References 44 publications

Simulation of Full Wavefield Data with Deep Learning Approach for Delamination Identification

Simulation of Full Wavefield Data with Deep Learning Approach for Delamination Identification

Enhanced Global Convolution Networks for Defect Detection in Full Wavefield Imaging

An explainable artificial intelligence‐based approach for reliable damage detection in polymer composite structures using deep learning

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