Acoustic Emission Waveform Analysis Research Articles

Statistics of acoustic emission waveforms in characterizing the fracture process zone in fibre-reinforced cementitious materials under mode I fracture

This article reports on the characteristics of fracture process zone in steel fibre-reinforced concrete (SFRC) under the mode I fracture process using acoustic emission (AE) testing. The generated AE waveforms during mode I fracture process in SFRC were recorded in the laboratory. Using a statistical analysis of AE waveforms, it was observed that as the loading increases, a damage zone consisting of numerous microcracks develops ahead of the predefined notch tip. The location of the generated AE events related to the numerous microcracks were classified into three zones namely (i) major damage, (ii) moderate damage and (iii) low damage. The areas of these regions were evaluated from the distribution of the AE events around the pre-notch. The number of AE events reduced with the increase in the steel fibre content under the same experimental conditions. The major damage zone was located ahead of the notch tip very closely and it comprised of AE events with (i) high peak amplitude, (ii) low information entropy and (iii) longer AE waveform duration.

Acoustic Emission Waveform Analysis for Instability and Failure Evaluation of Snow

Acoustic Emission Waveform Analysis for Instability and Failure Evaluation of Snow

An Artificial Intelligence Approach to Fatigue Crack Length Estimation from Acoustic Emission Waves in Thin Metallic Plates

The acoustic emission (AE) technique has become a well-established method of monitoring structural health over recent years. The sensing and analysis of elastic AE waves, which have involved piezoelectric wafer active sensors (PWAS) and time domain and frequency domain analysis, has proven to be effective in yielding fatigue crack-related information. However, not much research has been performed regarding (i) the correlation between the fatigue crack length and AE signal signatures and (ii) artificial intelligence (AI) methodologies to automate the AE waveform analysis. In this paper, this crack length correlation is investigated along with the development of a novel AE signal analysis technique via AI. A finite element model (FEM) study was first performed to understand the effects of fatigue crack length on the resulting AE waveforms and a fatigue experiment was performed to capture experimental AE waveforms. Finally, this database of experimental AE waveforms was used with a convolutional neural network to build a system capable of performing automated classification and prediction of the length of a fatigue crack that excited respective AE signals. AE signals captured during a fatigue crack growth experiment were found to match closely with the FEM simulations. This novel AI system proved to be effective at predicting the crack length of an AE signal at an accuracy of 98.4%. This novel AI-enabled AE signal analysis technique will provide a crucial step forward in the development of a comprehensive structural health monitoring (SHM) system.

Acoustic Emission Waveform Analysis of Sandstone Failure with Different Water Content

Previous studies have shown that water can reduce the acoustic emission (AE) energy and other parameters during rock failure. However, the fracture mechanism of rock can be better reflected by analyzing the AE waveform. Therefore, this paper conducted experiments of uniaxial compression on sandstone samples of various water contents and collected AE signals simultaneously. Analyses of fast Fourier transform (FFT) and Hilbert-Huang transform (HHT) were performed on the AE waveform when the sample failed. The results show that as the water content increases, the frequency and intensity of the AE signal will decrease. The influence of water on the intensity of the AE signal is greater than that on the frequency. Through the analysis of the energy mechanism of rock failure, it is pointed out that the frequency and intensity of AE signal are closely related to elastic energy index W ET and burst energy index K E . The research results have guiding significance for the monitoring of rockburst.

An Experimental Investigation of Moisture-Induced Softening Mechanism of Marble Based on Quantitative Analysis of Acoustic Emission Waveforms

The decrease of strength after saturation of rocks is known as moisture-induced softening. To date, there are numerous studies on the mechanism of moisture-induced softening of different rocks. However, due to a lack of effective observational methods, the microcosmic mechanism of moisture-induced softening still needs to be understood. We collected and processed acoustic emission (AE) signals during the uniaxial compression test of marble specimens. The results of spectral and statistical analysis show that two dominant frequency bands of AE waveforms exist regardless of the specimen’s water content. Additionally, for the AE signals from the saturated specimens, the ranges of the low and high frequency bands are wider than dried rock samples. Besides, since the tensile and shear failures in the rock release low and high dominant frequency AE signals, respectively, the test results of this paper show that micro-shear and micro-tensile failures dominate the final failure of dried and saturated rocks, respectively.

An Experimental Investigation of the Failure Mechanisms of Jointed and Intact Marble under Compression Based on Quantitative Analysis of Acoustic Emission Waveforms

An Experimental Investigation of the Failure Mechanisms of Jointed and Intact Marble under Compression Based on Quantitative Analysis of Acoustic Emission Waveforms

Acoustic emission waveform analysis in CFRP under Mode I test

In this paper the capability of acoustic emission (AE) to monitor delamination in unidirectional CFRP subjected to mode I loading conditions was tested. Double cantilever beams made by epoxy resin reinforced by carbon unidirectional fiber were tested. By following the ASTM D5528-01 standard the laminates have an even number of plies and delamination grows up following the 0° horizontal direction. Specimens were prepared so that an insert was placed in the mid-plane acting as an initiation site for delamination. Test were carried out at room temperature and at constant displacement rate. Delamination evolution was followed in two ways: first of all it was monitored by putting a CCD camera which follows the damage propagation by observing at the sample through a proper reference grid sized by following the indications of the ASTM D5528-01 standard. At the same time a piezoelectric sensor was placed on the top of each specimen and coupled by a silicon gel. The wave-sound generated inside the sample during the test was recorded by the sensor and pre-amplified before being recorded by the PAC-PCI-2 data acquisition system. Main features of the acoustic signals were analyzed such as hits number, energy and amplitude. Moreover, a wavelet analysis on the recorded waveforms was performed in order to find possible correlation between the frequency content of the acoustic signal and the damage evolution.

Acoustic emission characterization of failure mechanisms in oriented strand board using wavelet-based and unsupervised clustering methods

The acoustic emission (AE) technique has been used for decades to detect damage onset and propagation in complex materials such as composites. In this research, acoustic emission along with mechanical testing were applied for a better understanding and characterizing of micro- and macrostructural damage mechanisms leading to overall failure and fracture of specimens. For this purpose, strands and also unidirectional laboratory-made oriented strand board specimens were loaded under tensile and bending loads. Parametric analysis of AE, unsupervised pattern recognition algorithm of k-means, and wavelet packet transform were applied to the analysis of the acquired data. The results of the analysis of AE waveforms revealed that the fingerprint of frequency range of strands AE response has been detected in the strand board specimens. The data analysis led to detection and discrimination of different failure mechanisms such as microcracks of strands, strands failure, debonding, and delamination of strand boards using acoustic emission technique. The frequency ranges of strand failure mechanisms, local debonding of strands, and delamination were defined as 0–200, 200–300, and 300–400 kHz, respectively.

Automated valve fault detection based on acoustic emission parameters and support vector machine

Reciprocating compressors are one of the most used types of compressors with wide applications in industry. The most common failure in reciprocating compressors is always related to the valves. Therefore, a reliable condition monitoring method is required to avoid the unplanned shutdown in this category of machines. Acoustic emission (AE) technique is one of the effective recent methods in the field of valve condition monitoring. However, a major challenge is related to the analysis of AE signal which perhaps only depends on the experience and knowledge of technicians. This paper proposes automated fault detection method using support vector machine (SVM) and AE parameters in an attempt to reduce human intervention in the process. Experiments were conducted on a single stage reciprocating air compressor by combining healthy and faulty valve conditions to acquire the AE signals. Valve functioning was identified through AE waveform analysis. SVM faults detection model was subsequently devised and validated based on training and testing samples respectively. The results demonstrated automatic valve fault detection model with accuracy exceeding 98%. It is believed that valve faults can be detected efficiently without human intervention by employing the proposed model for a single stage reciprocating compressor.

Health monitoring of Ceramic Matrix Composites from waveform-based analysis of Acoustic Emission

Ceramic Matrix Composites (CMCs) are anticipated for use in the hot section of aircraft engines. Their implementation requires the understanding of the various damage modes that are involved and their relation to life expectancy. Acoustic Emission (AE) has been shown to be an efficient technique for monitoring damage evolution in CMCs. However, only a waveform-based analysis of AE can offer the possibility to validate and precisely examine the recorded AE data with a view to damage localization and identification. The present work fully integrates wave initiation, propagation and acquisition in the analysis of Acoustic Emission waveforms recorded at various sensors, therefore providing more reliable information to assess the relation between Acoustic Emission and damage modes. The procedure allows selecting AE events originating from damage, accurate determination of their location as well as the characterization of effects of propagation on the recorded waveforms. This approach was developed using AE data recorded during tensile tests on carbon/carbon composites. It was then applied to melt-infiltrated SiC/SiC composites.

Evaluation of damage accumulation behavior and strength anisotropy of NITE SiC/SiC composites by acoustic emission, digital image correlation and electrical resistivity monitoring

Understanding the cracking process of the composites is essential to establish the design basis for practical applications. This study aims to investigate the damage accumulation process and its anisotropy for nano-infiltration transient eutectic sintered (NITE) SiC/SiC composites by various characterization techniques such as the acoustic emission (AE), digital image correlation (DIC) and electrical resistivity (ER) measurements. Cracking behavior below the proportional limit stress (PLS) was specifically addressed. Similar to the other generic SiC/SiC composites, the 1st AE event was identified below the PLS for NITE SiC/SiC composites with a dependency of fabric orientation. The DIC results support that the primary failure mode depending on fiber orientation affected more than the other minor modes did. Detailed AE waveform analysis by wavelet shows a potential to classify the failure behavior depending on architecture. Cracking below the PLS is a potential concern in component deign but the preliminary ER measurements imply that the impact of cracking below the PLS on composite function was limited.

A novel approach for classification of failure modes in single lap joints using acoustic emission data

New innovative basalt fiber/epoxy composite materials are used in engineering applications such as aerospace, automotive, and civil structures due to the potential low cost of this material together with its mechanical characteristics and its failure mechanisms. Acoustic emission is a passive nondestructive testing technique for real-time monitoring of damage developed in materials and structures, which have been used successfully for the identification of damage mechanisms in composite joints under tensile loading. The present study is focussed on acoustic emission characterization of failure modes in three prominent joining methods namely, bonded, riveted, and hybrid joints during tensile test. Parametric analysis is performed on the acoustic emission data obtained during the tensile testing of these types of joints to discriminate the failure modes. Fast Fourier transform analysis using acoustic emission waveform analysis is carried out to analyze the different failure events and associate them with their dominant frequency ranges. The predominance of failure modes in each signal is used as a key in the study to discriminate failure modes on single-lap joints in basalt/epoxy composite laminate, and the results are validated with fast Fourier Transform analysis.

Development of Acoustic Emission Waveform Simulation Technique Utilizing a Sensor Response and Finite-Difference Time-Domain Method

Waveform simulation techniques are widely used for nondestructive inspections using elastic waves. They are also used for the acoustic emission (AE) technique. Source dynamics can be identified by AE waveform analysis. However, waveforms detected by AE sensors are deformed by the sensor response, and their characteristics depend on the frequency response and sensitivity; thus, accurate AE waveform simulation is difficult because of these problems. In this study, accurate AE waveform simulation involving a sensor response, using a finite-difference time-domain (FDTD) method was developed. The original waveform is calculated by the 2D FDTD method. The sensor response is determined by extraction of the input waves to the AE sensor by a pulse generator and detection by a laser interferometer. To include the sensor width and dispersion of the sensor response, some points of the sensor response on the sensor face are detected. These are convoluted, and the sensor responses of all points are added. After Lamb wave simulation, characteristics of the simulation waveforms were correlated to the artificial AE waveform produced by Hsu-Nielsen sources (pencil lead breaking). AE signals from mode I fracture were simulated next. The fracture was excited by exfoliation between a polyester plate and glass bar embedded in the plate. The characteristics of simulation waveform by developed technique corresponded to the waveform detected by the AE sensor.

Fatigue Process Evaluation of Ultrasonic Fatigue Testing in High Strength Steel Analyzed by Acoustic Emission and Non-Linear Ultrasonic

Non-linear ultrasonic and acoustic emission (AE) signals during ultrasonic fatigue testing were analyzed by using Laser Doppler Vibrometer (LDV) and continuous AE waveform analysis system (1 MHz/12 bit). Notched specimens of a high strength low alloy steel were prepared for the ultrasonic fatigue test with exciting vibration frequency of 20 kHz. The detected surface velocity was longitudinal direction at the end of specimen with frequency range from 200 Hz to 500 kHz. During the waveform monitoring of the fatigue test, a distorted exciting waveform was observed in final stage of the test. Then the burst type noise mixed with the distorted exciting waveform was obtained just before the final failure in the case of failure specimens. Contrary, the distorted exciting waveform and AE were not observed in the case of non-failure specimens. AE signal and upper harmonics of exciting frequency were analyzed by the FFT method. As the result, after the intensity of 2nd and 3rd harmonics increased rapidly, AE events were detected continuously in the case of failure specimens. It can be concluded that nonlinear ultrasonic and AE analysis were effective monitoring tool for fatigue damage progression.

超音波疲労試験時の AE・非線形超音波解析による高強度鋼の疲労進展評価

Non-linear ultrasonic and acoustic emission (AE) signals during ultrasonic fatigue testing were analyzed by using Laser Doppler Vibrometer (LDV) and continuous AE waveform analysis system (1 MHz/12bit). Notched specimens of a high strength low alloy steel were prepared for the ultrasonic fatigue test with exciting vibration frequency of 20 kHz. The detected surface velocity was longitudinal direction at the end of the specimen with frequency range from 100 Hz to 500 kHz. During the waveform monitoring of the fatigue test, a distorted exciting waveform was observed in the final stage of the test. Then the burst type noise mixed with the distorted exciting waveform was obtained just before the final failure in the case of the failure specimens. Contrary, the distorted exciting waveform and AE were not observed in the case of non-failure specimens. AE signal and upper harmonics of exciting frequency were analyzed by the FFT method. As the result, after the intensity of 2nd and 3rd harmonics increased rapidly, AE events were detected continuously in the case of the failure specimens. It can be concluded that non-linear ultrasonic and AE analysis were effective monitoring tool for fatigue damage progression.

Dynamic Behavior of Two Types of Martensitic Transformations in Cu-Al-Ni Shape Memory Alloy Single Crystal by Acoustic Emission Method

Dynamic behavior of two types of martensitic transformations during tensile deformation of Cu-Al-Ni shape memory alloy single crystal has been investigated using an acoustic emission waveform analysis. Two kinds of martensitic transformations consist of β1 ⇔ β1′ (structural change of DO3 to 18R) and β1 ⇒ γ1′ (structural change of DO3 to 2H), each of which is called super-elastic and thermo-elastic martensitic transformations, respectively. These two types of martensitic transformations could be obtained during tensile deformation because of different heat treatment. The rise time at the source (the source rise time) in finite elastic solid by the modified Takashima’s method was analyzed using the acoustic emission waveform detected during the martensitic transformation. The mean source rise time to the γ1′ phase was smaller than that to the β1′ phase before yielding and became the same after yielding. The former result means that the nucleation of the γ1′ phase is faster than that of the β1′ phase because of different crystallographic structure. The latter result is that the growth rate of the γ1′ phase is the same as that of the β1′ phase.

境界要素法に基づいたアコースティック・エミッション波形解析

The boundary element method (BEM) is extensively applied to numerical analysis. Because the formulation represents solutions of the governing equations, the method is applicable to analyze a variety of quantitative measurements. Acoustic emission (AE) is a phenomenon where elastic waves are detected due to microcracking. Thus, BEM formulation of elastodynamics is applied to quantitative waveform analysis of AE waveforms. Results of the inverse analysis are discussed on creak kinetics and kinematics.

Acoustic emission evaluation of an arch dam during construction cooling and grouting

Acoustic emission (AE) monitoring was conducted in order to ensure safe construction of an arch dam under severe climate conditions. The AE was detected during the secondary cooling in the winter time while the construction was suspended due to low temperature and heavy snow. In the spring time just before the construction restarted, AE was monitored during grouting into a joint of dam blocks. A multi-channel digital signal processing system was used to perform a quantitative AE waveform analysis based on the moment tensor analysis as well as the conventional AE parameter measurement. Low frequency AE sensors (15 kHz resonant) were employed to detect AE signals since the signals with ordinary frequencies attenuate greatly during the propagation in concrete of the dam. Twelve sensors were buried from the dam surface and four were mounted on the corridor floor inside the dam. Neither critical AE activity nor intense AE cluster were observed in the AE monitoring. Thus, it was confirmed that the dam was safe during both the secondary cooling and the grouting. The AE monitoring was proven to be very useful for the safety inspection of a dam under construction in severe conditions.

Improved accuracy and reliability of estimates of defect locations by detailed analysis of acoustic emission waveforms

Improved accuracy and reliability of estimates of defect locations by detailed analysis of acoustic emission waveforms

Acoustic emission waveform analysis to identify fatigue crack propagation in a mirage aircraft: Journal of Acoustic Emission, Vol. 6, No. 4, pp. 249–256 (Oct.–Dec. 1987)

Acoustic emission waveform analysis to identify fatigue crack propagation in a mirage aircraft: Journal of Acoustic Emission, Vol. 6, No. 4, pp. 249–256 (Oct.–Dec. 1987)