Defect sizing in guided wave imaging structural health monitoring using convolutional neural networks

Miorelli, Roberto; Fisher, Clément; Kulakovskyi, Andrii; Chapuis, Bastien; Mesnil, Olivier; D’Almeida, Oscar

doi:10.1016/j.ndteint.2021.102480

Cited by 46 publications

(13 citation statements)

References 28 publications

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“…Combining physical models with data models to establish a nonlinear mapping relationship between signal input and damage assessment can compensate for the shortcomings of traditional damage detection [ 120 ]. ML can be applied in several steps of ultrasonic Lamb wave damage detection, from the judgment of existence [ 121 ] to classification [ 122 ], localization [ 123 ], size assessment [ 124 ], depth reconstruction [ 125 ], and shape recognition [ 126 ]. The operational process can be summarized as obtaining detection information, extracting and selecting features, and classifying actual cases according to the categories that have been assigned labels.…”

Section: Detection Methods Based On the Small Amount Of Wavefield Datamentioning

confidence: 99%

A Review of Laser Ultrasonic Lamb Wave Damage Detection Methods for Thin-Walled Structures

Zheng

Luo

et al. 2023

Sensors

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Thin-walled structures, like aircraft skins and ship shells, are often several meters in size but only a few millimeters thick. By utilizing the laser ultrasonic Lamb wave detection method (LU-LDM), signals can be detected over long distances without physical contact. Additionally, this technology offers excellent flexibility in designing the measurement point distribution. The characteristics of LU-LDM are first analyzed in this review, specifically in terms of laser ultrasound and hardware configuration. Next, the methods are categorized based on three criteria: the quantity of collected wavefield data, the spectral domain, and the distribution of measurement points. The advantages and disadvantages of multiple methods are compared, and the suitable conditions for each method are summarized. Thirdly, we summarize four combined methods that balance detection efficiency and accuracy. Finally, several future development trends are suggested, and the current gaps and shortcomings in LU-LDM are highlighted. This review builds a comprehensive framework for LU-LDM for the first time, which is expected to serve as a technical reference for applying this technology in large, thin-walled structures.

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Section: Detection Methods Based On the Small Amount Of Wavefield Datamentioning

confidence: 99%

A Review of Laser Ultrasonic Lamb Wave Damage Detection Methods for Thin-Walled Structures

Zheng

Luo

et al. 2023

Sensors

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“…Introducing more meaningful features to the network can lead to a more understandable and credible model. In order to overcome this drawback, Miorelli et al 19 proposed the ShmGwi-InvNet exploiting a CNN architecture to invert damage size and location. Visualizing the shallowest and deepest layers feature maps reflects some degrees of interpretability.…”

Section: Interpretable Lamb Wave Convolutional Sparse Coding Methods ...mentioning

confidence: 99%

“…Finite element-based simulations have been used to augment the training dataset. [19][20][21] The match between simulations and experiments must be sufficient. In addition, a well-behaved deep CNN model often requires appropriate layers with a large number of parameters for solving a complex problem, which might increase computational costs of network training.…”

Section: Introductionmentioning

confidence: 99%

Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization

Zhang

Lin

Hua

et al. 2021

Structural Health Monitoring

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Lamb wave-based damage identification and localization methods hold the potential for nondestructive evaluation and structural health monitoring. Dispersive and multimodal characteristics lead to complicated Lamb wave signals that are challenging to be analyzed. Deep learning architectures could identify damage-related features effectively. Convolutional neural network (CNN) is a promising architecture that has been widely applied in recent years. However, this data-driven approach still lacks a certain degree of physical interpretability and requires a large number of parameters. In this article, an interpretable Lamb wave convolutional sparse coding (LW-CSC) method is proposed for structural damage identification and localization. First, toneburst signals at different center frequencies are considered in the first convolutional layer. The network convolves the waveforms with a set of parametrized functions that implement band-pass filters, which performs more physical interpretability compared to conventional CNN model. Subsequently, the damage features are extracted according to the multi-layer iterative soft thresholding algorithm for multi-layer CSC model, which could realize a deeper network without adding parameters unlike CNN. Finally, Lamb wave-based damage localization is visualized using an imaging algorithm. The experimental results demonstrate that the proposed method not only enables improvement of the classification accuracy but also achieves structural damage localization accurately.

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“…This work demonstrates that the use of physics-based models can boost the performance of DL models. Miorelli et al [60] proposed a CNN for automating the defect localisation and sizing from ultrasonic guided waves. The input data consist of DAS images from circular piezoelectric transducer layouts and the output of the model is the continuous values of the XY position and radius of the defects in aluminium plates.…”

Section: Regressorsmentioning

confidence: 99%

“…Bai et al [61] has recently provided a comparative study between classical Bayesian inversion approaches and a CNN regressor model -i.e. the SMInvNet based on the model developed by Miorelli [60]. Here, the input data consist of the scattering matrices (measured from FMC and TFM data) while the output is the size and angle of a surface notch.…”

Section: Regressorsmentioning

confidence: 99%

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

Cantero-Chinchilla,

Wilcox,

Croxford

2021

Preprint

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The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data acquisition, data pre-processing, defect detection, and defect characterisation) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data; moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve full artificial intelligence driven automation of ultrasonic NDE so that the research community, industry, and regulatory bodies embrace them. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.

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Defect sizing in guided wave imaging structural health monitoring using convolutional neural networks

Cited by 46 publications

References 28 publications

A Review of Laser Ultrasonic Lamb Wave Damage Detection Methods for Thin-Walled Structures

A Review of Laser Ultrasonic Lamb Wave Damage Detection Methods for Thin-Walled Structures

Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

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