Relation between Amplitude and Duration of Acoustic Emission Signals

Pumarega, María Isabel López; Armeite, M.; Ruzzante, José; Piotrkowski, Rosa

doi:10.1063/1.1570299

Cited by 11 publications

(3 citation statements)

References 2 publications

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“…Acoustic emission from grating can be minimized by collecting and analyzing signals just around the peak load [2,9]. The spatial filtering techniques based on the source location, guard transducers [5,16] and time of arrival [17] can be employed to separate extraneous noise from crack bursts in laboratory testing to understand the mechanism of acoustic emission associated with crack growth behavior, based on the prediction that the crack initiates ahead of the notch tip and grows along the notch plane. However, the techniques used to minimize extraneous noise in laboratory testing may not be entirely applicable to acoustic emission bridge-monitoring because the crack location, the environmental noise and loading amplitudes can be less predictable in the field.…”

Section: Steel Bridge Monitoring With Acoustic Emissionmentioning

confidence: 99%

Adaptation of PWAS transducers to acoustic emission sensors

Momeni

Godinez

et al. 2011

SPIE Proceedings

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Piezoelectric wafer active sensors (PWAS) are non-intrusive transducers that can convert mechanical energy into electrical energy, and vice versa. They are well known for their dual use as either actuators or sensors. Though PWAS has shown great potential for active sensing, its capability for acoustic emission (AE) detection has not yet been exploited. In the reported work, we have explored the implementation of PWAS transducers for both passive (AE sensors) and active (in-situ ultrasonic transducers) sensing using a single PWAS network. The objective of the work presented in this paper is to adapt PWAS as AE sensors and compare it to the commercially available AE transducers such as PAC R15.An experiment has been designed to show how PWAS can be used for AE detection and the results were compared to a standard AE sensor, PAC R15I. Tests on compact tension specimens have also been conducted to show PWAS capability to pick up AE events during fatigue loading. PWAS field installation technology has been tested with packaging similar to that used for traditional strain gauges. The performance of packaged PWAS has been compared with that of conventional AE transducers R15I. We have found that PWAS not only can detect the presence of AE events but also can provide a wide frequency bandwidth. At this stage, PWAS underperforms the commercial AE sensors. To make PWAS ready for field test, signal to noise ratio needs to be significantly improved.

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Section: Steel Bridge Monitoring With Acoustic Emissionmentioning

confidence: 99%

Adaptation of PWAS transducers to acoustic emission sensors

Momeni

Godinez

et al. 2011

SPIE Proceedings

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“…Next, for the search work of the end of the core of the hit, it is required to find the maximum value 𝑆𝑇𝐸 𝑚𝑎𝑥−𝑐𝑜𝑟𝑒 regarding to the provisional core of the hit (i.e., the range between STE[provisional onset hit] and STE[provisional end of the core of the hit]), to then readjust the Identification Lower Threshold (ITL) expressed in levels of energy (v 2 .s). For this, it is necessary to start from the fact that for the Acoustic Emission discipline one of the most accepted models [27][28][29][30][31][32][33][34] of an electrically transduced AE wave, considers to the wave as an underdamped sinusoidal function with the form:…”

Section: Yesmentioning

confidence: 99%

An acoustic emission activity detection method based on short-term waveform features: Application to metallic components under uniaxial tensile test

Moctezuma

Delgado-Prieto

Romeral-Martinez

2020

Mechanical Systems and Signal Processing

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The Acoustic Emission (AE) phenomenon has been used as a powerful tool with the purpose to either detect, locate or assess damage for a wide range of applications. Derived from its monitoring, one major current challenge on the analysis of the acquired signal is the proper identification and separation of each AE event. Current advanced methods for detecting events are primarily focused on identifying with high accuracy the beginning of the AE wave; however, the detection of the conclusion has been disregarded in the literature. For an automatic continuous detection of events within a data stream, this lack of accuracy for the conclusion of the events generates errors in two critical aspects. In one hand, it deteriorates the accuracy of the measurement of the events duration, truncating the span of the event (undesirable in evaluation applications), and in the other hand, it causes false detections. In this work, an accurate and computationally efficient AE activity detector is presented, using a framework inspired by the area of speech processing, and which provides the required indicators to accurately detect the onset and the end of an AE event. This is achieved by means of a threshold approach that instead of directly operates with the transduced voltage signal it does so over the Short-Term Energy and the Short-Term Zero-Crossing Rate measures of the signal. The STE-ZCR method is developed for an application related to the continuous monitoring of a single AE channel derived from the characterization of metallic components by means of an uniaxial tensile test. Additionally, two experimental test-benches are implemented with the aim to quantify the accuracy and the quality of event detection of the presented method. Finally, the obtained results are compared with four different techniques, representing the current state of the art related to AE activity detection.

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“…Identification Lower Threshold (ITL) expressed in levels of energy (v 2 .s). For this, it is necessary to start from the fact that for the Acoustic Emission discipline one of the most accepted models [65,[97][98][99][100][101][102][103] of an electrically transduced AE wave, considers to the wave as an underdamped sinusoidal function with the form:…”

Section: Ste[provisional Onset Hit] and Ste[provisional End Of The Co...mentioning

confidence: 99%

Characterization of damage evolution on metallic components using ultrasonic non-destructive methods

Piñal Moctezuma

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When fatigue is considered, it is expected that structures and machinery eventually fail. Still, when this damage is unexpected, besides of the negative economic impact that it produces, life of people could be potentially at risk. Thus, nowadays it is imperative that the infrastructure managers, ought to program regular inspection and maintenance for their assets; in addition, designers and materials manufacturers, can access to appropriate diagnostic tools in order to build superior and more reliable materials. In this regard, and for a number of applications, non-destructive evaluation techniques have proven to be an efficient and helpful alternative to traditional destructive assays of materials. Particularly, for the design area of materials, in recent times researchers have exploited the Acoustic Emission (AE) phenomenon as an additional assessing tool with which characterize the mechanical properties of specimens. Nevertheless, several challenges arise when treat said phenomenon, since its intensity, duration and arrival behavior is essentially stochastic for traditional signal processing means, leading to inaccuracies for the outcome assessment. In this dissertation, efforts are focused on assisting in the characterization of the mechanical properties of advanced high strength steels during under uniaxial tensile tests. Particularly of interest, is being able to detect the nucleation and growth of a crack throughout said test. Therefore, the resulting AE waves generated by the specimen during the test are assessed with the aim of characterize their evolution. For this, on the introduction, a brief review about non-destructive methods emphasizing the AE phenomenon is introduced. Next is presented, an exhaustive analysis with regard to the challenge and deficiencies of detecting and segmenting each AE event over a continuous data-stream with the traditional threshold detection method, and additionally, with current state of the art methods. Following, a novel AE event detection method is proposed, with the aim of overcome the aforementioned limitations. Evidence showed that the proposed method (which is based on the short-time features of the waveform of the AE signal), excels the detection capabilities of current state of the art methods, when onset and endtime precision, as well as when quality of detection and computational speed are also considered. Finally, a methodology aimed to analyze the frequency spectrum evolution of the AE phenomenon during the tensile test, is proposed. Results indicate that it is feasible to correlate nucleation and growth of a crack with the frequency content evolution of AE events. Cuando se considera la fatiga de los materiales, se espera que eventualmente las estructuras y las maquinarias fallen. Sin embargo, cuando este daño es inesperado, además del impacto económico que este produce, la vida de las personas podría estar potencialmente en riesgo. Por lo que hoy en día, es imperativo que los administradores de las infraestructuras deban programar evaluaciones y mantenimientos de manera regular para sus activos. De igual manera, los diseñadores y fabricantes de materiales deberían de poseer herramientas de diagnóstico apropiadas con el propósito de obtener mejores y más confiables materiales. En este sentido, y para un amplio número de aplicaciones, las técnicas de evaluación no destructivas han demostrado ser una útil y eficiente alternativa a los ensayos destructivos tradicionales de materiales. De manera particular, en el área de diseño de materiales, recientemente los investigadores han aprovechado el fenómeno de Emisión Acústica (EA) como una herramienta complementaria de evaluación, con la cual poder caracterizar las propiedades mecánicas de los especímenes. No obstante, una multitud de desafíos emergen al tratar dicho fenómeno, ya que el comportamiento de su intensidad, duración y aparición es esencialmente estocástico desde el punto de vista del procesado de señales tradicional, conllevando a resultados imprecisos de las evaluaciones. Esta disertación se enfoca en colaborar en la caracterización de las propiedades mecánicas de Aceros Avanzados de Alta Resistencia (AAAR), para ensayos de tracción de tensión uniaxiales, con énfasis particular en la detección de fatiga, esto es la nucleación y generación de grietas en dichos componentes metálicos. Para ello, las ondas mecánicas de EA que estos especímenes generan durante los ensayos, son estudiadas con el objetivo de caracterizar su evolución. En la introducción de este documento, se presenta una breve revisión acerca de los métodos existentes no destructivos con énfasis particular al fenómeno de EA. A continuación, se muestra un análisis exhaustivo respecto a los desafíos para la detección de eventos de EA y las y deficiencias del método tradicional de detección; de manera adicional se evalúa el desempeño de los métodos actuales de detección de EA pertenecientes al estado del arte. Después, con el objetivo de superar las limitaciones presentadas por el método tradicional, se propone un nuevo método de detección de actividad de EA; la evidencia demuestra que el método propuesto (basado en el análisis en tiempo corto de la forma de onda), supera las capacidades de detección de los métodos pertenecientes al estado del arte, cuando se evalúa la precisión de la detección de la llegada y conclusión de las ondas de EA; además de, cuando también se consideran la calidad de detección de eventos y la velocidad de cálculo. Finalmente, se propone una metodología con el propósito de evaluar la evolución de la energía del espectro frecuencial del fenómeno de EA durante un ensayo de tracción; los resultados demuestran que es posible correlacionar el contenido de dicha evolución frecuencial con respecto a la nucleación y crecimiento de grietas en AAAR's.

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Relation between Amplitude and Duration of Acoustic Emission Signals

Cited by 11 publications

References 2 publications

Adaptation of PWAS transducers to acoustic emission sensors

Adaptation of PWAS transducers to acoustic emission sensors

An acoustic emission activity detection method based on short-term waveform features: Application to metallic components under uniaxial tensile test

Characterization of damage evolution on metallic components using ultrasonic non-destructive methods

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