Baseline-free guided wave damage detection with surrogate data and dictionary learning

Alguri, K. Supreet; Melville, Joseph; Harley, Joel B.

doi:10.1121/1.5042240

Cited by 38 publications

(20 citation statements)

References 36 publications

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“…This choice was made to keep the tube safe (i.e., healthy) in order to use it in other experiments, and to repeat any previous task if the corresponding result was not satisfying. Such a method for simulating damage was applied in different research works [21,22]. Actually, defect occurrence in a structure can be geometric (i.e., local change of the shape of the structure) or material (i.e., local change of the stiffness of the material).…”

Section: Experiments and Database Buildingmentioning

confidence: 99%

A Semi-Supervised Based K-Means Algorithm for Optimal Guided Waves Structural Health Monitoring: A Case Study

et al. 2019

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This paper concerns the health monitoring of pipelines and tubes. It proposes the k-means clustering algorithm as a simple tool to monitor the integrity of a structure (i.e., detecting defects and assessing their growth). The k-means algorithm is applied on data collected experimentally, by means of an ultrasonic guided waves technique, from healthy and damaged tubes. Damage was created by attaching magnets to a tube. The number of magnets was increased progressively to simulate an increase in the size of the defect and also, a change in its shape. To test the performance of the proposed method for damage detection, a statistical population was created for the healthy state and for each damage step. This was done by adding white Gaussian noise to each acquired signal. To optimize the number of clusters, many algorithms were run, and their results were compared. Then, a semi-supervised based method was proposed to determine an alarm threshold, triggered when a defect becomes critical.

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Section: Experiments and Database Buildingmentioning

confidence: 99%

A Semi-Supervised Based K-Means Algorithm for Optimal Guided Waves Structural Health Monitoring: A Case Study

et al. 2019

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“…Different damage detection methods were developed and widely documented the literature [1]- [4]. In particular, the use of ultrasonic guided waves has proved to be one of the promising methods for detecting damage [5]- [8], specifically, with respect to the fundamental modes of Lamb [9][10] and non-dispersive Rayleigh waves [11]- [13]. Other studies were largely focused on understanding of guided wave interactions with and scattering at various types of defects [14]- [16].…”

Section: Structural Health Monitoring Using Guided Wavesmentioning

confidence: 99%

“…wavenumber. Substitute the equation to the equation of motion yields the Christoffel equations 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 K e< U < = 0 (8) and the parameters K e< are given by…”

Section: Governing Equations For Acoustoelastic Lamb Wave Propagationmentioning

confidence: 99%

Finite element prediction of acoustoelastic effect associated with Lamb wave propagation in pre-stressed plates

Yang

Mohabuth

et al. 2019

Smart Mater. Struct.

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The paper presents outcomes of a finite element (FE) study of acoustoelastic effect associated with Lamb wave propagation in plates subjected to homogeneous bi-axial and bending stresses.In particular, the change of the phase velocity of the fundamental Lamb wave modes is obtained for different stress levels, bi-axial stress ratios and wave propagation angles. A comparison of the obtained numerical results with an analytical solution demonstrates a very good agreement.Moreover, the influence of bending stress on the wave velocities and wave front profile is further investigated numerically. There are currently no analytical results for this case. The developed and validated FE modelling approach can help to address several issues in the current non-destructive inspections including: in the investigation of changing stress conditions on the defect detection as well as in an adaptation of the existing Lamb wave-based defect evaluation systems to monitoring of stress too. The latter may have many benefits from sharing the same hardware for the purpose of maintaining structural integrity of thin-walled structural components. . Acoustoelastic effect of Lamb wave propagationAcoustoelastic effect is defined as the effect of the applied stress on the wave propagation in a media. It has been studied since the development of the finite deformation theory by Murnaghan [40], who formulated the material nonlinearity using third order elastic constants. Some pioneering studies in this area include the research of Hughes and Kelly [41], who derived equations relating the wave velocity to the applied stress and experimentally measured the acoustoelastic effect. Another experimental study of Egle and Bray [42] demonstrated how to obtain higher-order elastic constants from experiments with bulk waves. In the literature, many developments and studies in the acoustoelasticity mainly focused on and utilised bulk waves (Pau and Scalea [43]). The acoustoelastic effect is usually quantified = (20) So, equation (19), with the energy function presented in equation (16) can be translated to, ¬ = J -. © © = J -. © © © = J -. %)( ) % ©where T is the second Piola-Kirchhoff (PK2) stress. The stress in VUMAT must be updated with the equation at the end ( + ∆ ) of an integration step and stored in stressNew(i), based on the values of F and U given in the subroutine at the end of previous step ( ). Numerical Case Studies 3D Finite Element ModelA 3D FE model of a 6061-T6 aluminium plate was developed with ABAQUS software and the wave propagation problem was solved by the explicit integration approach [49]. The materialproperties of the 6061-T6 aluminium are listed in Table 1. The thickness of the plate is 3.2mm and the in-plane dimension is 240mm×240mm. The element type used in the model is the 8-

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“…Likewise, a CNN has been tested to estimate CT.Th and bulk velocities using simulated ultrasonic guided waves [44]. In addition, similar machine learning-based approaches have also been recently introduced in non destructive testing in case of guided wave damage detection [45] or localization and characterization [46].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classifying of Patients With Non-Traumatic Fractures Based on Ultrasonic Guided Wave Spectrum Image Using a Dynamic Support Vector Machine

et al. 2020

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Bone fractures are caused by diseases or accidents and are a widespread problem throughout the globe. Worldwide, 1.6 millions of hip fractures occur every year and are expected to rise to 6.3 millions in 2050. The current gold standard to assess fracture risk is the Dual-energy X-ray Absorptiometry (DXA), which provides a projected image of the bone from which areal bone mineral density is extracted. Ultrasound techniques have been proposed as non invasive alternatives. Recently, estimates of cortical thickness and porosity, obtained by Bi-Directional Axial Transmission (BDAT) in a pilot clinical study, have been shown to be associated with non-traumatic fractures in post menopausal women. Cortical parameters were derived from the comparison between experimental and theoretical guided modes. This model-based inverse approach failed for the patients associated with poor guided mode information. Moreover, even if the fracture discrimination ability was found similar to DXA, it remained moderate. The goal of this paper is to explore if these two limitations could be overcome by using automatic classification tools, independent of any waveguide model. To this end, a dynamic machine learning approach based on a Support Vector Machine (SVM) has been used to classify ultrasonic guided wave spectrum images measured by BDAT on post menopausal women with or without non-traumatic fractures. This approach has then been improved using parameters tuned by Bat Algorithm Optimization (BOA). The applied methodology focused on the extraction of texture features through a gray level co-occurrence matrix, structural comparison and histograms. The results accuracy was assessed using a confusion matrix. The effectiveness of the learning approach reached an accuracy of 92.31%. INDEX TERMS cortical bone, osteoporosis, fracture discrimination, quantitative ultrasound, guided waves, support vector machine, bat algorithm, guided wave spectrum image.

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Baseline-free guided wave damage detection with surrogate data and dictionary learning

Cited by 38 publications

References 36 publications

A Semi-Supervised Based K-Means Algorithm for Optimal Guided Waves Structural Health Monitoring: A Case Study

A Semi-Supervised Based K-Means Algorithm for Optimal Guided Waves Structural Health Monitoring: A Case Study

Finite element prediction of acoustoelastic effect associated with Lamb wave propagation in pre-stressed plates

Automatic Classifying of Patients With Non-Traumatic Fractures Based on Ultrasonic Guided Wave Spectrum Image Using a Dynamic Support Vector Machine

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