Improving of Acoustic Emission Signal Detection for Fatigue Fracture Monitoring

Danyuk, A. V.; Растегаев, И. А.; Pomponi, Eraldo; Linderov, Mikhail; Мерсон, Д. Л.; Vinogradov, Alexei

doi:10.1016/j.proeng.2017.02.323

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…Using the short-time Fourier transform, Unnthorson proposed a fully automatic hit detector method [26], [39]. However, most current research focuses on the use of the wavelet transform (WT) [17], [40]- [43], which improves the resolution of the energy localization of the AE event in the time-frequency plane, increasing the accuracy of onset determination.…”

Section: Advanced Acoustic Emission Hit Detection Methodsmentioning

confidence: 99%

Performance Analysis of Acoustic Emission Hit Detection Methods Using Time Features

2019

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Acoustic emission (AE) analysis is a powerful potential characterization method for fracture mechanism analysis during metallic specimen testing. Nevertheless, identifying and extracting each event when analyzing the raw signal remains a major challenge. Typically, the AE detection is carried out using a thresholding approach. However, though extensively applied, this approach presents some critical limitations due to overlapping transients, differences in strength and low signal-to-noise ratio. In this paper, to address these limitations, advanced methodologies for detecting AE hits have been developed. The most prominent methodologies used are instantaneous amplitude, the short-term average to long-term average ratio, the Akaike information criterion, and wavelet analysis, each of which exhibits satisfactory performance and ease of implementation for diverse applications. However, their proneness to errors in the presence of non-cyclostationary AE wavefronts and the lack of thorough comparison for transient AE signals are constraints to the wider application of these methods in non-destructive testing procedures. In this paper, with the aim of making aware of the drawbacks of the traditional threshold approach, a comprehensive analysis of its limiting factors when taking into regard the AE waveform behavior is presented. In addition, in a second section, a performance analysis of the main advanced representative-methods in the field is carried out through a common comparative framework, by analyzing first, AE waves generated from a standardized Hsu-Nielsen test and second, a data frame of a highly active signal derived from a tensile test. In this paper, with the aim to quantify the performance with which these AE detection methodologies work, for the first time, time features as the endpoint and duration accuracies, as well as statistical metrics as accuracy, precision, and false detection rates, are studied.INDEX TERMS Acoustic emission, materials testing, AE thresholding method, short-term average to longterm average ratio, instantaneous amplitude, Akaike information criterion, wavelet analysis, Otsu's method.

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Section: Advanced Acoustic Emission Hit Detection Methodsmentioning

confidence: 99%

Performance Analysis of Acoustic Emission Hit Detection Methods Using Time Features

2019

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“…Table 5 reveals just the data collected during the first stage. To eliminate the noise attributed to the use of electronic transducers, a threshold of >40 dB was provided, based on the amplitudes evidenced in different AE sources [20], [21]. Under said threshold, during the background noise determination period, a waveform with patterns that repeat constantly and also, low amplitude, energy and duration, different from the transient pulses characteristic of an AE, were noticed [12].…”

Section: Methodsmentioning

confidence: 99%

Failure detection in a pressure vessel using acoustic emissions technology

Peñaloza-Peña

Galvis

Quiroga

2019

revuin

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En este artículo se implementó la técnica de emisiones acústicas (EA) para monitorear diferentes escenariosde falla en cilindros de acero usados para almacenar gas licuado del petróleo (GLP). Los experimentos se llevaron a cabo en dos recipientes con defectos externos e internos sometidos a una prueba hidrostática. La severidad de las fallas fue aumentando progresivamente a través de los distintos escenarios con el fin de estudiar la actividad microsísmica. Las pruebas experimentales permitieron comprobar los efectos Káiser -Felicity y se observó que un aumento en la severidad de la falla representaba un incremento en la cantidad de hits detectados, cuentas y valores de energía. La evolución de la actividad acústica para los diferentes escenarios de falla determinó la integridad estructural de los contenedores, demostrándose que las EA permiten evaluar la condición global de los cilindros.

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“…A signal‐based analysis may be used to postprocess each single recorded waveform. If for example, the signal to noise ratio of the AE acquisition is low, subsequent filtering or complex algorithms can significantly improve the evaluation . However, it must be recognized that this also leads to a distortion of the signal.…”

Section: Techniques To Study the Trigger Mechanismmentioning

confidence: 99%

Validation of Self‐Healing Properties of Construction Materials through Nondestructive and Minimal Invasive Testing

Snoeck

Malm

Cnudde

et al. 2018

Adv Materials Inter

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When studying the self‐healing properties of construction materials, a plethora of destructive and nondestructive testing (NDT) techniques can be used. In this review, the applicability of different nondestructive test methods is discussed in detail. The methods can be categorized whether they are used to study the encapsulation and/or protection mechanism of the healing agent, the sequestered healing agent itself, the distribution of healing agents, the trigger mechanism for healing, the healing efficiency, or healing performance. Based on this categorization, nondestructive techniques found in literature are discussed. In this way, a robust understanding of the different techniques can be used for future research on self‐healing construction materials.

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Improving of Acoustic Emission Signal Detection for Fatigue Fracture Monitoring

Cited by 9 publications

References 9 publications

Performance Analysis of Acoustic Emission Hit Detection Methods Using Time Features

Performance Analysis of Acoustic Emission Hit Detection Methods Using Time Features

Failure detection in a pressure vessel using acoustic emissions technology

Validation of Self‐Healing Properties of Construction Materials through Nondestructive and Minimal Invasive Testing

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