scholarly journals Acoustic Emission Wave Velocity Measurement of Asphalt Mixture by Arbitrary Wave Method

2021 ◽  
Vol 11 (18) ◽  
pp. 8505
Author(s):  
Jianfeng Li ◽  
Huifang Liu ◽  
Wentao Wang ◽  
Kang Zhao ◽  
Zhoujing Ye ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Wave Velocity ◽  
Velocity Measurement ◽  
Measurement Method ◽  
Asphalt Mixture ◽  
Low Frequency ◽  
Noise Signal ◽  
New Wave ◽  
Ae Signal ◽  
First Arrival

The wave velocity of acoustic emission (AE) can reflect the properties of materials, the types of AE sources and the propagation characteristics of AE in materials. At the same time, the wave velocity of AE is also an important parameter in source location calculation by the time-difference method. In this paper, a new AE wave velocity measurement method, the arbitrary wave (AW) method, is proposed and designed to measure the AE wave velocity of an asphalt mixture. This method is compared with the pencil lead break (PLB) method and the automatic sensor test (AST) method. Through comparison and analysis, as a new wave velocity measurement method of AE, the AW method shows the following advantages: A continuous AE signal with small attenuation, no crosstalk and a fixed waveform can be obtained by the AW method, which is more advantageous to distinguish the first arrival time of the acoustic wave and calculate the wave velocity of AE more accurately; the AE signal measured by the AW method has the characteristics of a high frequency and large amplitude, which is easy to distinguish from the noise signal with the characteristics of a low frequency and small amplitude; and the dispersion of the AE wave velocity measured by the AW method is smaller, which is more suitable for the measurement of the AE wave velocity of an asphalt mixture.

scholarly journals Damage evolution in wood – pattern recognition based on acoustic emission (AE) frequency spectra

Holzforschung ◽  
2015 ◽  
Vol 69 (3) ◽  
pp. 357-365 ◽  
Author(s):  
Franziska Baensch ◽  
Markus G.R. Sause ◽  
Andreas J. Brunner ◽  
Peter Niemz
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Cluster Formation ◽  
Low Frequency ◽  
Tensile Tests ◽  
Most Probable Number ◽  
Frequency Spectra ◽  
Probable Number ◽  
Ae Signal ◽  
Ae Signals

Abstract Tensile tests on miniature spruce specimens have been performed by means of acoustic emission (AE) analysis. Stress was applied perpendicular (radial direction) and parallel to the grain. Nine features were selected from the AE frequency spectra. The signals were classified by means of an unsupervised pattern recognition approach, and natural classes of AE signals were identified based on the selected features. The algorithm calculates the numerically best partition based on subset combinations of the features provided for the analysis and leads to the most significant partition including the respective feature combination and the most probable number of clusters. For both specimen types investigated, the pattern recognition technique indicates two AE signal clusters. Cluster A comprises AE signals with a relatively high share of low-frequency components, and the opposite is true for cluster B. It is hypothesized that the signature of rapid and slow crack growths might be the origin for this cluster formation.

Research and Analysis on Rock Acoustic Emission Wave Characteristics

2014 ◽  
Vol 638-640 ◽  
pp. 534-537
Author(s):  
Jian Ping He
Keyword(s):  
Acoustic Emission ◽  
Wavelet Analysis ◽  
Laboratory Tests ◽  
Low Frequency ◽  
Development Trend ◽  
Signal Wave ◽  
Original Signal ◽  
Great Efficiency ◽  
Wave Characteristics ◽  
Ae Signal

Analyzing wave behavior on acoustic emission laboratory tests from relationship between stress and AE rate , the rock AE signal wave in laboratory is decomposed into high and low frequency elements. Analysis and compare with the details elements include, the approximation elements and original AE wave, the results show that rock AE wave characteristics are not same in the course of transform fracture on stages, found AE wave characteristics storehouse, it is a matter of great significance for utilizing monitoring and prediction regularity and development trend in the course of fracture transform. The error between the original signal and the signal of wave coefficient of the approximation elements reconstructed is minor to, wavelet analysis makes accuracy and reliability of rock AE wave characteristics monitoring and prediction improved, States that wavelet analysis is a great efficiency method.

scholarly journals Fracture Damage Properties of SBS-Modified Asphalt Mixtures Reinforced with Basalt Fiber after Freeze-Thaw Cycles Using the Acoustic Emission Approach

2020 ◽  
Vol 10 (9) ◽  
pp. 3301 ◽  
Author(s):  
Chunyu Liang ◽  
Junchen Ma ◽  
Peilei Zhou ◽  
Guirong Ma ◽  
Xin Xu
Keyword(s):  
Acoustic Emission ◽  
High Temperature ◽  
Asphalt Mixture ◽  
Basalt Fiber ◽  
Load Level ◽  
Asphalt Mixtures ◽  
Fracture Characteristics ◽  
Freeze Thaw ◽  
Ae Signal ◽  
Fracture Damage

This paper focuses on the fracture damage characteristics of styrene-butadiene-styrene (SBS)-modified SMA-13 specimens with basalt fiber under various freeze-thaw (F-T) cycles. SBS-modified stone mastic asphalt (SMA)-13 specimens with basalt fiber were prepared, first, using the superpave gyratory compaction method. Then, asphalt mixture specimens processed with 0–21 F-T cycles were adopted for the high-temperature compression and low-temperature splitting tests. Meanwhile, the acoustic emission (AE) test was conducted to evaluate the fracture characteristics of the asphalt mixture during loading. The results showed that the AE parameters could effectively reflect the damage fracture characteristics of the asphalt mixture specimen during the high-temperature compression and low-temperature splitting processes. The fracture damage of the asphalt mixture specimens during compression or splitting are classified into three stages based on the variation of the AE signals, i.e., when the load level is below 0.1~0.2 during the first stage and the load level is 0.1–0.9 or 0.2–0.8 during the second stage. The AE signal amplitude and count show clear correlations with the compression and splitting load levels. Meanwhile, the AE signal clarifies the formation, development, and failure of internal damage for the asphalt mixture specimens during the compression and splitting processes. The intensity (value and density) of the AE signal parameters of asphalt mixture decreases with increasing F-T cycles. It is evident that the F-T cycle has a significant adverse effect on the mechanical strength of asphalt mixture, which makes asphalt mixtures more likely to cause early failure.

scholarly journals A New Fracture Detection Algorithm of Low Amplitude Acoustic Emission Signal Based on Kalman Filter-Ripple Voltage

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4247
Author(s):  
Seong-Min Jeong ◽  
Seokmoo Hong ◽  
Jong-Seok Oh
Keyword(s):  
Acoustic Emission ◽  
Kalman Filter ◽  
Noise Signal ◽  
High Frequency Component ◽  
Fracture Detection ◽  
Forming Process ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Voltage Signal ◽  
Ae Signal

In this study, an acoustic emission (AE) sensor was utilized to predict fractures that occur in a product during the sheet metal forming process. An AE activity was analyzed, presuming that AE occurs when plastic deformation and fracturing of metallic materials occur. For the analysis, a threshold voltage is set to distinguish the AE signal from the ripple voltage signal and noise. If the amplitude of the AE signal is small, it is difficult to distinguish the AE signal from the ripple voltage signal and the noise signal. Hence, there is a limitation in predicting fractures using the AE sensor. To overcome this limitation, the Kalman filter was used in this study to remove the ripple voltage signal and noise signal and then analyze the activity. However, it was difficult to filter out the ripple voltage signal using a conventional low-pass filter or Kalman filter because the ripple voltage signal is a high-frequency component governed by the switch-mode of the power supply. Therefore, a Kalman filter that has a low Kalman gain was designed to extract only the ripple voltage signal. Based on the KF-RV algorithm, the measured ripple voltage and noise signal were reduced by 97.3% on average. Subsequently, the AE signal was extracted appropriately using the difference between the measured value and the extracted ripple voltage signal. The activity of the extracted AE signal was analyzed using the ring-down count among various AE parameters to determine if there was a fracture in the test specimen.

Investigation and utilization of the acoustic emission signal for monitoring the dressing process

2002 ◽  
Vol 216 (4) ◽  
pp. 543-553 ◽  
Author(s):  
M Wehmeier ◽  
I Inasaki
Keyword(s):  
Acoustic Emission ◽  
Cubic Boron Nitride ◽  
Low Frequency ◽  
Frequency Component ◽  
Grinding Wheel ◽  
Emission Signal ◽  
Continuous Scanning ◽  
Conventional Grinding ◽  
Ae Signal ◽  
User Friendly

Improvement of monitoring techniques is essential to make the complex dressing process more reliable, economical and user friendly. A system utilizing data from acoustic emission (AE) sensors and a suitable algorithm combined with a graphical user interface has been applied with this aim. The influence of different grinding wheel types and dressing parameters on the AE signal has been investigated and a dressing monitoring system is proposed. The root mean square (r.m.s.) signal, or the low-frequency component of the AE signal, provides information that can be utilized to monitor the process. A reliable touch detection and cycle termination can be established. The spectrum of the raw signal has been investigated for cubic boron nitride (CBN) and conventional grinding wheels. Reliable data acquisition techniques, which make a continuous scanning of such wide bandwidth signals possible, have been applied.

scholarly journals Study on Acoustic Emission Localization of Concrete Using Modified Velocity

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jiapeng Li ◽  
Lei Qin
Keyword(s):  
Acoustic Emission ◽  
Wave Velocity ◽  
Source Localization ◽  
Wave Speed ◽  
Localization Algorithm ◽  
Measured Velocity ◽  
3D Localization ◽  
Before And After ◽  
Ae Signal ◽  
Ae Location

To improve the accuracy of the acoustic emission (AE) source localization, a new 3D AE source localization method was investigated which used a combination of the modified velocity and the 3D localization algorithm based on the exhaustive method. The wave speed has a significant effect on the AE location results. With the increase of distance, the AE signal seriously attenuated due to the anisotropy of concrete, and the measured velocity changed for various distances. The velocity-distance curves were obtained when employing different water-cement ratios (W/C) and aggregate sizes. However, the current AE location system adopted constant wave velocity. As a result, the error was tremendous. The accuracy of the localization before and after the modified velocity was compared. The 3D localization results showed that compared with the constant wave velocity, the position deviation of the modified velocity was smaller and the localization results with the modified velocity were more accurate.

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