MULTIFRACTAL CHARACTERISTICS AND ACOUSTIC EMISSION OF COAL WITH JOINTS UNDER UNIAXIAL LOADING

In order to explore the causes of acoustic emission (AE) signals during coal failure, the coal samples with original joints were subjected to uniaxial compression experiments, and the AE signals were monitored by AEwin Test for Express-8.0. Based on the multifractal theory, the multifractal characteristics of AE were analyzed. The results showed that the AE counts and accumulative counts change over time corresponded well with the load-time, which reflected the degree of crack evolution and loading. During the initial loading stage, the cracks expanded gradually along the trace of the original cracks, which could induce a few AE events, while with the increase of load, the cracks enlarged gradually and then joined together to form a macroscopic fracture, which would cause much more AE events within a larger value. Multifractal spectrum [[Formula: see text]] of AE was more concentrated in the right side, illustrating that the frequency of small signals was greater than that of the large signals in AE sequences, which revealed cracks expanding and microfracture events dominated during the loading process. The greater the multifractal spectrum width ([Formula: see text] was, the larger the AE signals differences were, which reflected that AE varied more intensely. The more developed the original cracks, the more obvious the multifractal characteristics. This research revealed the causes and percentage of the AE events within small or large signals, which would help us to recognize crack evolution of coal and generation mechanism of AE.

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NONLINEAR CHARACTERISTICS OF ACOUSTIC EMISSION DURING THE HEATING PROCESS OF COAL AND ROCK

Fractals ◽

10.1142/s0218348x18500469 ◽

2018 ◽

Vol 26 (04) ◽

pp. 1850046 ◽

Cited By ~ 2

Author(s):

ZHIBO ZHANG ◽

ENYUAN WANG ◽

ENLAI ZHAO ◽

SHUAI YANG

Keyword(s):

Acoustic Emission ◽

Hurst Exponent ◽

Multifractal Spectrum ◽

Heating Process ◽

Nonlinear Characteristics ◽

Rock Samples ◽

Spectrum Width ◽

Multifractal Theory ◽

Ae Signal ◽

Temperature Levels

In this paper, acoustic emission (AE) signal of coal and rock samples during the heating process are measured. The results show that AE energy of coal samples is higher than that of rock samples. Based on the multifractal theory, the multifractal characteristics of AE signal are researched. The multifractal spectrum width ([Formula: see text]) of coal samples is wider than that of rock samples, which means AE signal of coal samples is more complex than AE signal of rock samples during the heating process. Multifractal parameter ([Formula: see text]) is more than zero, illustrating that small AE signal is dominate. The time-varying multifractal characteristics are analyzed, and the change trend of multifractal spectrum width ([Formula: see text]) of coal and rock samples is consistent. At the stage of 40–50[Formula: see text]C, multifractal spectrum width ([Formula: see text]) gets the maximum value, whereas multifractal spectrum width ([Formula: see text]) gets the minimum value at the stage of 80–100[Formula: see text]C. For coal samples, multifractal parameter ([Formula: see text] is more than zero except at the stage of 40–50[Formula: see text]C. However, multifractal parameter ([Formula: see text] of rock samples is always more than zero during the entire heating process. By [Formula: see text] analytical method, Hurst exponent of AE signal is calculated. The results show that Hurst exponent of coal and rock samples are more than 0.5, which indicate that AE signal presents persistence, and there is a positive correction between AE signal and temperature. In different temperature levels, Hurst exponent curve presents an increase trend after the initial decrease.

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FRACTALS AND CHAOS CHARACTERISTICS OF ACOUSTIC EMISSION ENERGY ABOUT GAS-BEARING COAL DURING LOADED FAILURE

Fractals ◽

10.1142/s0218348x19500725 ◽

2019 ◽

Vol 27 (05) ◽

pp. 1950072 ◽

Cited By ~ 27

Author(s):

XIANGGUO KONG ◽

ENYUAN WANG ◽

SHUGANG LI ◽

HAIFEI LIN ◽

PENG XIAO ◽

...

Keyword(s):

Acoustic Emission ◽

Chaotic Behavior ◽

High Stress ◽

Multifractal Spectrum ◽

High Energy ◽

Time Varying ◽

Multifractal Theory ◽

Coal Damage ◽

Gas Bearing ◽

Ae Signals

To study the damage evolution mechanism of gas-bearing coal and formation causes of acoustic emission signals during this process, the loaded experiments of gas-bearing coal were performed, and acoustic emission (AE) data radiated in this process were collected. Based on the multifractal theory, the causes of AE were explored in various loaded phases. The results showed that at the low stress stage, the fractures close and the friction/slip could cause low-energy acoustic emission events, and the multifractal spectrum had a smaller width. By contrast, at the high stress stage, the cracks expand, penetrate, and rupture, which would lead to AE events with the release of high energy, reflecting an increase in the width of the multifractal spectrum. At the initial loading stage, the time-varying multifractal spectrum was characterized by a chaotic behavior, but as the loading progressed, it gradually became orderly. In the elastic stage, coal experienced elastic deformation without damage, the ratio of strong and weak AE signals was almost the same, and both [Formula: see text] and [Formula: see text] were close to 0. In the plastic fracture stage, coal body consumed huge amounts of energy and suffered fracture. This also caused the coal body to radiate a large amount of AE signals. An analysis of these signals indicated that strong signals dominated and showed an increasing trend, and [Formula: see text] was less than 0 and continued to decrease. The time-varying multifractal characteristics reveal the formation mechanism of AE signals from gas-bearing coal, which contributes to improve our understanding of the mechanism of gas-bearing coal damage.

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Damage evolution in wood: synchrotron radiation micro-computed tomography (SRμCT) as a complementary tool for interpreting acoustic emission (AE) behavior

Holzforschung ◽

10.1515/hf-2014-0152 ◽

2015 ◽

Vol 69 (8) ◽

pp. 1015-1025 ◽

Cited By ~ 18

Author(s):

Franziska Baensch ◽

Michaela Zauner ◽

Sergio J. Sanabria ◽

Markus G.R. Sause ◽

Bernd R. Pinzer ◽

...

Keyword(s):

Computed Tomography ◽

Acoustic Emission ◽

Synchrotron Radiation ◽

Damage Evolution ◽

Tensile Tests ◽

High Time Resolution ◽

Micro Computed Tomography ◽

Crack Evolution ◽

Microscopic Scale ◽

Ae Signals

Abstract Tensile tests of miniature spruce wood specimens have been performed to investigate the damage evolution in wood at the microscopic scale. For this purpose, the samples were stepwise tensile loaded in the longitudinal (L) and radial (R) directions and the damage evolution was monitored in real-time by acoustic emission (AE) and synchrotron radiation micro-computed tomography (SRμCT). This combination is of outstanding benefit as SRμCT monitoring provides an insight on the crack evolution and the final fracture at microscopic scale, whereas AE permits the detection of the associated accumulation and interaction of single damage events on all length scales with high time resolution. A significant drawback of the AE testing of wood has been overcome by means of calibrating the AE amplitudes with the underlying crack length development. Thus, a setup-dependent and wood species-dependent calibration value was estimated, which associates 1 μm2 crack area generating of 0.0038 mV in the detected AE amplitude. Furthermore, for both L and R specimens, AE signals were classified into two clusters by using a frequency-based approach of unsupervised pattern recognition. The shares of AE signals of both clusters correlate with the ratio of the relative crack area of the interwall and transwall cracks gained from the fractographic analysis of SRμCT scans.

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Improvement in prefrontal thalamic connectivity during the early course of the illness in recent-onset psychosis: a 12-month longitudinal follow-up resting-state fMRI study

Psychological Medicine ◽

10.1017/s0033291720004808 ◽

2020 ◽

pp. 1-9

Author(s):

Daniel Bergé ◽

Tyler A. Lesh ◽

Jason Smucny ◽

Cameron S. Carter

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Functional Changes ◽

Recent Onset ◽

Fmri Study ◽

Clinical Meaning ◽

Mixed Pattern ◽

The Right ◽

Over Time

Abstract Background Previous research in resting-state functional magnetic resonance imaging (rs-fMRI) has shown a mixed pattern of disrupted thalamocortical connectivity in psychosis. The clinical meaning of these findings and their stability over time remains unclear. We aimed to study thalamocortical connectivity longitudinally over a 1-year period in participants with recent-onset psychosis. Methods To this purpose, 129 individuals with recent-onset psychosis and 87 controls were clinically evaluated and scanned using rs-fMRI. Among them, 43 patients and 40 controls were re-scanned and re-evaluated 12 months later. Functional connectivity between the thalamus and the rest of the brain was calculated using a seed to voxel approach, and then compared between groups and correlated with clinical features cross-sectionally and longitudinally. Results At baseline, participants with recent-onset psychosis showed increased connectivity (compared to controls) between the thalamus and somatosensory and temporal regions (k = 653, T = 5.712), as well as decreased connectivity between the thalamus and left cerebellum and right prefrontal cortex (PFC; k = 201, T = −4.700). Longitudinal analyses revealed increased connectivity over time in recent-onset psychosis (relative to controls) in the right middle frontal gyrus. Conclusions Our results support the concept of abnormal thalamic connectivity as a core feature in psychosis. In agreement with a non-degenerative model of illness in which functional changes occur early in development and do not deteriorate over time, no evidence of progressive deterioration of connectivity during early psychosis was observed. Indeed, regionally increased connectivity between thalamus and PFC was observed.

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Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding

Applied Sciences ◽

10.3390/app11157045 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7045

Author(s):

Ming-Chyuan Lu ◽

Shean-Juinn Chiou ◽

Bo-Si Kuo ◽

Ming-Zong Chen

Keyword(s):

Stainless Steel ◽

Acoustic Emission ◽

Frequency Domain ◽

Monitoring System ◽

Welding Quality ◽

Steel Sheets ◽

Signal Features ◽

Ae Signal ◽

Laser Microwelding ◽

Ae Signals

In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwelding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval.

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Acoustic Emission Testing and Ib-Value Analysis of Ultraviolet Light-Irradiated Fiber Composites

Applied Sciences ◽

10.3390/app11146550 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6550

Author(s):

Doyun Jung ◽

Wonjin Na

Keyword(s):

Acoustic Emission ◽

Irradiation Time ◽

Tensile Load ◽

Failure Behavior ◽

Value Analysis ◽

Emission Testing ◽

Final Failure ◽

Acoustic Emission Testing ◽

Woven Glass Fiber ◽

Ae Signals

The failure behavior of composites under ultraviolet (UV) irradiation was investigated by acoustic emission (AE) testing and Ib-value analysis. AE signals were acquired from woven glass fiber/epoxy specimens tested under tensile load. Cracks initiated earlier in UV-irradiated specimens, with a higher crack growth rate in comparison to the pristine specimen. In the UV-degraded specimen, a serrated fracture surface appeared due to surface hardening and damaged interfaces. All specimens displayed a linearly decreasing trend in Ib-values with an increasing irradiation time, reaching the same value at final failure even when the starting values were different.

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Acoustic Emission Monitoring of Leaks in Pipes for Transport of Liquid and Gaseous Media: A Model Experiment

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.13-14.351 ◽

2006 ◽

Vol 13-14 ◽

pp. 351-356 ◽

Cited By ~ 12

Author(s):

Andreas J. Brunner ◽

Michel Barbezat

Keyword(s):

Acoustic Emission ◽

Model Experiment ◽

Structural Integrity ◽

Fiber Composite ◽

Leak Testing ◽

Pipe Segment ◽

Potential Applications ◽

Gaseous Media ◽

The Media ◽

Ae Signals

In order to explore potential applications for Active Fiber Composite (AFC) elements made from piezoelectric fibers for structural integrity monitoring, a model experiment for leak testing on pipe segments has been designed. A pipe segment made of aluminum with a diameter of 60 mm has been operated with gaseous (compressed air) and liquid media (water) for a range of operating pressures (between about 5 and 8 bar). Artificial leaks of various sizes (diameter) have been introduced. In the preliminary experiments presented here, commercial Acoustic Emission (AE) sensors have been used instead of the AFC elements. AE sensors mounted on waveguides in three different locations have monitored the flow of the media with and without leaks. AE signals and AE waveforms have been recorded and analysed for media flow with pressures ranging from about 5 to about 8 bar. The experiments to date show distinct differences in the FFT spectra depending on whether a leak is present or not.

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Radiation-Induced Metabolic Shifts in the Hepatic Parenchyma: Findings from 18F-FDG PET Imaging and Tissue NMR Metabolomics in a Mouse Model for Hepatocellular Carcinoma

Molecules ◽

10.3390/molecules26092573 ◽

2021 ◽

Vol 26 (9) ◽

pp. 2573

Author(s):

Yi-Hsiu Chung ◽

Cheng-Kun Tsai ◽

Ching-Fang Yu ◽

Wan-Ling Wang ◽

Chung-Lin Yang ◽

...

Keyword(s):

Fdg Pet ◽

Liver Parenchyma ◽

Metabolic Alterations ◽

Nmr Metabolomics ◽

Post Irradiation ◽

The Third ◽

Radiation Induced ◽

18F Fdg ◽

The Right ◽

Over Time

Purpose: By taking advantage of 18F-FDG PET imaging and tissue nuclear magnetic resonance (NMR) metabolomics, we examined the dynamic metabolic alterations induced by liver irradiation in a mouse model for hepatocellular carcinoma (HCC). Methods: After orthotopic implantation with the mouse liver cancer BNL cells in the right hepatic lobe, animals were divided into two experimental groups. The first received irradiation (RT) at 15 Gy, while the second (no-RT) did not. Intergroup comparisons over time were performed, in terms of 18F-FDG PET findings, NMR metabolomics results, and the expression of genes involved in inflammation and glucose metabolism. Results: As of day one post-irradiation, mice in the RT group showed an increased 18F-FDG uptake in the right liver parenchyma compared with the no-RT group. However, the difference reached statistical significance only on the third post-irradiation day. NMR metabolomics revealed that glucose concentrations peaked on day one post-irradiation both, in the right and left lobes—the latter reflecting a bystander effect. Increased pyruvate and glutamate levels were also evident in the right liver on the third post-irradiation day. The expression levels of the glucose-6-phosphatase (G6PC) and fructose-1, 6-bisphosphatase 1 (FBP1) genes were down-regulated on the first and third post-irradiation days, respectively. Therefore, liver irradiation was associated with a metabolic shift from an impaired gluconeogenesis to an enhanced glycolysis from the first to the third post-irradiation day. Conclusion: Radiation-induced metabolic alterations in the liver parenchyma occur as early as the first post-irradiation day and show dynamic changes over time.

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A Principal Component Analysis of Acoustic Emission Signals from a Landing Gear Component

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.13-14.41 ◽

2008 ◽

Vol 13-14 ◽

pp. 41-47 ◽

Cited By ~ 6

Author(s):

Rhys Pullin ◽

Mark J. Eaton ◽

James J. Hensman ◽

Karen M. Holford ◽

Keith Worden ◽

...

Keyword(s):

Principal Component Analysis ◽

Acoustic Emission ◽

Background Noise ◽

Principal Component ◽

Component Analysis ◽

Landing Gear ◽

Fracture Detection ◽

Artificial Fracture ◽

High Level ◽

Ae Signals

This work forms part of a larger investigation into fracture detection using acoustic emission (AE) during landing gear airworthiness testing. It focuses on the use of principal component analysis (PCA) to differentiate between fracture signals and high levels of background noise. An artificial acoustic emission (AE) fracture source was developed and additionally five sources were used to generate differing AE signals. Signals were recorded from all six artificial sources in a real landing gear component subject to no load. Further to this, artificial fracture signals were recorded in the same component under airworthiness test load conditions. Principal component analysis (PCA) was used to automatically differentiate between AE signals from different source types. Furthermore, successful separation of artificial fracture signals from a very high level of background noise was achieved. The presence of a load was observed to affect the ultrasonic propagation of AE signals.

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Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.329.15 ◽

2007 ◽

Vol 329 ◽

pp. 15-20 ◽

Cited By ~ 2

Author(s):

Xun Chen ◽

James Griffin

Keyword(s):

Neural Network ◽

Acoustic Emission ◽

Material Removal ◽

Grinding Process ◽

Short Time Fourier Transform ◽

Fundamental Investigation ◽

Mechanical Behaviours ◽

Wavelets Transform ◽

Short Time ◽

Ae Signals

The material removal in grinding involves rubbing, ploughing and cutting. For grinding process monitoring, it is important to identify the effects of these different phenomena experienced during grinding. A fundamental investigation has been made with single grit cutting tests. Acoustic Emission (AE) signals would give the information relating to the groove profile in terms of material removal and deformation. A combination of filters, Short-Time Fourier Transform (STFT), Wavelets Transform (WT), statistical windowing of the WT with the kurtosis, variance, skew, mean and time constant measurements provided the principle components for classifying the different grinding phenomena. Identification of different grinding phenomena was achieved from the principle components being trained and tested against a Neural Network (NN) representation.

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