Combined two-level damage identification strategy using ultrasonic guided waves and physical knowledge assisted machine learning

Rautela, Mahindra; Senthilnath, J.; Moll, Jochen; Gopalakrishnan, S.

doi:10.1016/j.ultras.2021.106451

Cited by 78 publications

(38 citation statements)

References 33 publications

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“…CNN is widely used to process data having grid-like topology or spatial sequences (images) and temporal sequences (time-series data). It has sparse connections and parameter sharing, which makes it distinct from a fully connected network (FCN) [42,43]. A CNN is constructed using convolutional and pooling layers, which helps it to extract useful features automatically at different levels of abstractions, as shown in Fig.…”

Section: Inverse Model: Dual-branch Cnnmentioning

confidence: 99%

Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion

Rautela,

Huber,

Senthilnath

et al. 2022

Preprint

Self Cite

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In this work, ultrasonic guided waves and a dual-branch version of convolutional neural networks are used to solve two different but related inverse problems, i.e., finding layup sequence type and identifying material properties. In the forward problem, polar group velocity representations are obtained for two fundamental Lamb wave modes using the stiffness matrix method. For the inverse problems, a supervised classification-based network is implemented to classify the polar representations into different layup sequence types (inverse problem -1) and a regression-based network is utilized to identify the material properties (inverse problem -2).

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Section: Inverse Model: Dual-branch Cnnmentioning

confidence: 99%

Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion

Rautela,

Huber,

Senthilnath

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…For instance, defect detection and characterisation were jointly addressed in a DL framework using a single convolutional network that gives all the defect-related output for ultrasonic guided-waves in aluminium plates [113]. Alternatively, the use of concatenated DL models have been explored for defect detection and characterisation using physical knowledge and ultrasonic guided-waves [114]. In general, having one or two separate networks for the automation of multiple steps has advantages and limitations.…”

Section: Level 2: Partial Automationmentioning

confidence: 99%

“…Having reviewed the state-of-the-art and the contributions made at each automation level, it is easy to appreciate how heterogeneous the current DL methodologies are from paper to paper. For instance, some authors propose models that address different steps simultaneously [113] while others do it with independent models [114]. Explicit model limitations (e.g.…”

Section: Basic Axioms For Dl-based Ultrasonic Ndementioning

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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“…The proposed method offers localization performance similar to SRP-PHAT, but reduces considerably the computational load. Many SHM studies exploit Convolution Neural Network (CNN) [39,40,41]. Rautela et al [41] obtain damage detection and localization accuracy of 99 % using 1D CNN.…”

Section: Introductionmentioning

confidence: 99%

“…Many SHM studies exploit Convolution Neural Network (CNN) [39,40,41]. Rautela et al [41] obtain damage detection and localization accuracy of 99 % using 1D CNN. In the signal pre-processing phase, they perform five operations: 1) band-pass filtering and visualization; 2) frequency preferencing; 3) signal augmentation with noise; 4) cross-statistical feature engineering; and 5) TOF.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Touch Localization on Ultrasonic Wave Touchscreen

Bahrami¹,

Moriot²,

Masson³

et al. 2022

Preprint

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Classification and regression employing a simple Deep Neural Network (DNN) are investigated to perform touch localization on a tactile surface using ultrasonic guided waves. A model is trained using data captured using a robotic finger, and validated with experiments conducted with human fingers. The proposed method provides satisfactory localization results for most human-machine interactions, with a mean error of 0.47 cm and standard deviation of 0.18 cm. The classification approach is also adapted to identify touches on an access control keypad layout, which leads to an accuracy of 97%. These results demonstrate that DNN-based methods are a viable alternative to signal processing-based approaches for accurate and robust touch localization using ultrasonic guided waves.

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Combined two-level damage identification strategy using ultrasonic guided waves and physical knowledge assisted machine learning

Cited by 78 publications

References 33 publications

Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion

Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion

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

Machine Learning for Touch Localization on Ultrasonic Wave Touchscreen

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