Acoustic emission analysis using Bayesian model selection for damage characterization in ceramic matrix composites

Shiraiwa, Takayuki; Ishikawa, Kazuki; Enoki, Manabu; Shinozaki, Ippei; Kanazawa, Susumu

doi:10.1016/j.jeurceramsoc.2020.03.035

Cited by 17 publications

(4 citation statements)

References 55 publications

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“…The literature review shows that the primary focus of NDT of concrete structures is on detection and localization of structural defects such as voids, cracks, estimation of concrete cover, and localization of rebar and prestressed tendons by utilizing time and frequency domain analysis and image processing algorithms. A few researchers have investigated statistical data driven approaches (Gaussian and Weibull mixture modelling) and machine learning to identify and characterize damage in materials like ceramic composites using AE parameters [30]. Similar AE parameter-based classification of subsurface damage in wind turbine blades, using GMM is reported in [31].…”

Section: Introductionmentioning

confidence: 97%

Gaussian Mixture Model-Based Classification of Corrosion Severity in Concrete Structures Using Ultrasonic Imaging

2023

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Corrosion in reinforced concrete (RC) structures is associated with a reduction of the rebar diameter, loss of interfacial bond, cracking, and eventual spalling and probable collapse of the structure. The negative effects of corrosion on structural safety, durability, and longevity imposes significant costs on the national economy. Therefore, planned non-destructive testing (NDT) of concrete structures is essential to enhance the safety and economic sustainability of infrastructure. Previous work by the research group has established the capability of the ultrasonic Synthetic Aperture Focusing Technique (SAFT) as a tool for detection of rebar corrosion. This work extends the previous research towards application of statistical learning for ascertaining the corrosion severity through analysis of SAFT images of the rebar. Using features extracted from images, a Gaussian mixture model (GMM) is implemented to classify the severity of corrosion damage to the rebar. The results from the research positively demonstrate the potential of the proposed technique as an enabler for decisions pertaining to maintenance and timely repair of concrete infrastructural assets.

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Section: Introductionmentioning

confidence: 97%

Gaussian Mixture Model-Based Classification of Corrosion Severity in Concrete Structures Using Ultrasonic Imaging

2023

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“…One of these methods is the method of acoustic emission (AE), based on the phenomenon of acoustic emission, which is the excitation of elastic vibrations of the material caused by the formation and development of defects [12][13][14][15][16]. These oscillations are recorded by receivers located on the structure surface.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to other methods of nondestructive testing, the AE method has the following advantages [12,[14][15][16]:…”

Section: Introductionmentioning

confidence: 99%

Diagnostics of Defect Detection in the Initial Stages of Structural Failure Using the Acoustic Emission Method of Control

Grazion¹,

Spiryagin²,

Ерофеев³

et al. 2022

Applied Engineering Letters

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The article presents the results of experimental estimation of the possibility of early registration of structural defects using the acoustic-emission method of control, based on the phenomenon of acoustic emission, which is the excitation of elastic vibrations of the material, caused by the formation and development of defects. To confirm the possibility of recording the processes of predestruction, such as the development of crack-like defects and plastic deformation using the acoustic-emission method of control, full-scale tests of the pressure vessel were carried out and the physical model of the shell-and-tube heat exchanger specially designed for this process was verified. It has been determined that the most informative frequency range for recording acoustic emission signals is the range of 100-200 kHz. The relationship of acoustic emission with the characteristics of defect development during plastic deformation has been studied. It was found that for reliable determination of the plastic deformation process, shared usage of amplitude and the count rate of acoustic emission is advisable.

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“…In the majority of publications, hyperparameters are generally set by finding clusters with the best compactness and separability using external indices such as the Davies-Bouldin or Dunn indices [29,36,37] without considering onsets. In some cases, the cumulative ferquency of each cluster is plotted to interpret the timeline or chronology of clusters, but with a linear-linear representation making the analysis of the onsets secondary in the interpretation [38,39,40]. Indeed, results are often unreadable because some clusters have much larger rate of occurrence than others.…”

Section: Introductionmentioning

confidence: 99%

Clustering acoustic emission data streams with sequentially appearing clusters using mixture models

Ramasso,

Denoeux,

Chevallier

2021

Preprint

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The interpretation of unlabeled acoustic emission (AE) data classically relies on general-purpose clustering methods. While several external criteria have been used in the past to select the hyperparameters of those algorithms, few studies have paid attention to the development of dedicated objective functions in clustering methods able to cope with the specificities of AE data. We investigate how to explicitly represent clusters onsets in mixture models in general, and in Gaussian Mixture Models (GMM) in particular. By modifying the internal criterion of such models, we propose the first clustering method able to provide, through parameters estimated by an expectationmaximization procedure, information about when clusters occur (onsets), how they grow (kinetics) and their level of activation through time. This new objective function accommodates continuous timestamps of AE signals and, thus, their order of occurrence. The method, called GMMSEQ, is experimentally validated to characterize the loosening phenomenon in bolted structure under vibrations. A comparison with three standard clustering methods on raw streaming data from five experimental campaigns shows that GMMSEQ not only provides useful qualitative information about the timeline of clusters, but also shows better performance in terms of cluster characterization. In view of developing an open acoustic emission initiative and according to the FAIR principles, the datasets and the codes are made available to reproduce the research of this paper 1 .

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Acoustic emission analysis using Bayesian model selection for damage characterization in ceramic matrix composites

Cited by 17 publications

References 55 publications

Gaussian Mixture Model-Based Classification of Corrosion Severity in Concrete Structures Using Ultrasonic Imaging

Gaussian Mixture Model-Based Classification of Corrosion Severity in Concrete Structures Using Ultrasonic Imaging

Diagnostics of Defect Detection in the Initial Stages of Structural Failure Using the Acoustic Emission Method of Control

Clustering acoustic emission data streams with sequentially appearing clusters using mixture models

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