scholarly journals Studies on deformation and fracture mehanism of paper part 5. Maximum amplitude distribution analysis of acoustic emission signals from the tensile testing of paper.

1992 ◽  
Vol 46 (3) ◽  
pp. 442-446
Author(s):  
Tatsuo Yamauchi ◽  
Koji Murakami
Keyword(s):  
Acoustic Emission ◽  
Tensile Testing ◽  
Maximum Amplitude ◽  
Amplitude Distribution ◽  
Distribution Analysis ◽  
Deformation And Fracture

Amplitude-distribution analysis of acoustic emission

1979 ◽  
Vol 19 (12) ◽  
pp. 438-443 ◽  
Author(s):  
D. Dilipkumar ◽  
V. S. R. Gudimetla ◽  
W. E. Wood
Keyword(s):  
Acoustic Emission ◽  
Amplitude Distribution ◽  
Distribution Analysis

Wear Process Description Based on Acoustic Emission

1990 ◽  
Vol 112 (3) ◽  
pp. 469-476 ◽  
Author(s):  
B. E. Klamecki ◽  
J. Hanchi
Keyword(s):  
Acoustic Emission ◽  
Wear Behavior ◽  
Acoustic Emissions ◽  
Amplitude Distribution ◽  
Wear Process ◽  
Acoustic Emission Count ◽  
Deformation And Fracture ◽  
Emission Activity ◽  
Active Processes ◽  
Insight Into

Since acoustic emissions are generated by fundamental mechanical processes, they can provide insight into the basic processes which determine friction and wear behavior. Descriptions of acoustic emission generated by plastic deformation and fracture were developed, and wear tests were performed, during which acoustic emission activity was measured. This work demonstrates that acoustic emissions can be used to track the wear process in terms of the energy dissipation mechanisms acting. The results show that acoustic emission count rate and amplitude distribution correspond to wear rate and that the amplitude distribution also indicates the active processes contributing to wear.

scholarly journals STUDY ACOUSTIC EMISSION SIGNALS AT TENSION STEEL 20

2021 ◽  
Vol 83 (2) ◽  
pp. 188-197
Author(s):  
A.V. Ilyakhinskii ◽  
V.M. Rodyushkin ◽  
D.A. Ryabov ◽  
A.A. Khlybov ◽  
V.I. Erofeev
Keyword(s):  
Acoustic Emission ◽  
Tensile Testing ◽  
Dirichlet Distribution ◽  
Failure Process ◽  
Testing Machine ◽  
Amplitude Distribution ◽  
Self Organization ◽  
Uniaxial Tensile ◽  
Uniaxial Tensile Testing ◽  
Steel 20

An investigation was made of acoustic emission signals during uniaxial tensile testing of flat specimens of steel 20 used for parts of welded structures with a large volume of welding, as well as pipelines, collectors and other parts operating at temperatures from –40 to 450 °C under pressure. Tensile testing with simultaneous registration of acoustic emission was carried out on a universal testing machine manufactured by Tinius OIlsen Ltd, model H100KU, at a movement speed of the active gripper of 0.05 meters per minute. Registration of AE signals was carried out using wideband GT350 sensors from GlobalTest and an analog-to-digital converter NationalInstruments 6363X with subsequent storage of the registration results in the form of a time series in the computer memory. A comparative analysis of the amplitude distribution of the AE signal for the area of the yield area and the area of destruction was carried out according to the value of information entropy, fractal dimension, and self-organization parameter. It was found that the parameter of self-organization of the amplitude distribution of the signal is the most informative in describing the processes associated with acoustic emission. As additional information, it is advisable to use data on the structure of the self-organization parameter. The results obtained indicate the possibility of using the statistical model of the Dirichlet distribution as a model of processes associated with the appearance of acoustic emission signals from sources of incipient and developing defects during routine tests of products made of structural carbon high-quality steels with a pearlite-ferrite structure. The paper presents a version of the model and modeling algorithms for FE-modeling corrosion cracking processes in structural elements loaded by pressure and exposed to aggressive corrosion media. To assess the effectiveness of the present models and algorithms, the failure process of a thin-walled tubular specimen partly submerged into a chlorine-containing liquid and loaded by axial tension is numerically modeled.

scholarly journals Research on the Failure Evolution Process of Rock Mass Base on the Acoustic Emission Parameters

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaohui Liu ◽  
Xiaoping Zhao ◽  
Shishu Zhang ◽  
Ran Congyan ◽  
Rui Zhao
Keyword(s):  
Acoustic Emission ◽  
Rock Mass ◽  
Stress Level ◽  
Fractured Rock ◽  
Maximum Amplitude ◽  
Amplitude Distribution ◽  
Evolution Process ◽  
Single Fracture ◽  
Failure Evolution ◽  
Coal Rock

Fracture mechanics behavior and acoustic emission (AE) characteristics of fractured rock mass are related to underground engineering safety construction, disaster prediction, and early warning. In this study, the failure evolution characteristics of intact and fracture (e.g., single fracture, parallel fractures, cross fractures, and mixed fractures) coal were studied and contrasted with each other on the basis of the distribution of max amplitude of AE. The study revealed some meaningful results, where the value of b (i.e., the distribution characteristic of max amplitude of AE) could represent the failure evolution process of intact and fractured coal. The maximum amplitude distribution of AE events was characterized by Gaussian normal distribution, and the probability of the maximum amplitude of AE events corresponding to 35∼50 dB was the largest. In the stress range of 60∼80%, AE events and maximum amplitude increased rapidly, and the corresponding b value decreased. The energy of AE events showed a downward trend after reaching the maximum value at about 80% stress level. Under the same stress level, the more complex the fracture was, the larger the b value of coal–rock mass was, and the stronger the inhibition effect on the fracture expansion caused by the internal fracture distribution was. Due to the anisotropy of coal–rock mass with a single crack, the distribution of the b value was more discrete, while the anisotropy of coal–rock mass with mixed crack decreased, and the dispersion of the b value decreased. The deformation of cracked coal mainly caused by the adjustment of cracks during the initial loading b value experienced a trend of decreasing first, then increasing, and then decreasing in the loading process. When the load reached 0.8 times of the peak strength, the b value had a secondary decreasing trend, indicating the macroscopic failure of the sample, which could be used as a precursor criterion for the complete failure of coal–rock mass.

scholarly journals The duration-energy-size enigma for acoustic emission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Blai Casals ◽  
Karin A. Dahmen ◽  
Boyuan Gou ◽  
Spencer Rooke ◽  
Ekhard K. H. Salje
Keyword(s):  
Acoustic Emission ◽  
Mean Field Theory ◽  
Mean Field ◽  
Maximum Amplitude ◽  
Time Profile ◽  
Power Law Distribution ◽  
Model Predictions ◽  
Ae Signal ◽  
Strain Signal ◽  
Better Than

AbstractAcoustic emission (AE) measurements of avalanches in different systems, such as domain movements in ferroics or the collapse of voids in porous materials, cannot be compared with model predictions without a detailed analysis of the AE process. In particular, most AE experiments scale the avalanche energy E, maximum amplitude Amax and duration D as E ~ Amaxx and Amax ~ Dχ with x = 2 and a poorly defined power law distribution for the duration. In contrast, simple mean field theory (MFT) predicts that x = 3 and χ = 2. The disagreement is due to details of the AE measurements: the initial acoustic strain signal of an avalanche is modified by the propagation of the acoustic wave, which is then measured by the detector. We demonstrate, by simple model simulations, that typical avalanches follow the observed AE results with x = 2 and ‘half-moon’ shapes for the cross-correlation. Furthermore, the size S of an avalanche does not always scale as the square of the maximum AE avalanche amplitude Amax as predicted by MFT but scales linearly S ~ Amax. We propose that the AE rise time reflects the atomistic avalanche time profile better than the duration of the AE signal.

LONG RANGE CORRELATION PROPERTIES OF AFTERSHOCK RELAXATION SIGNALS

Fractals ◽  
1995 ◽  
Vol 03 (04) ◽  
pp. 839-847 ◽  
Author(s):  
A. VESPIGNANI ◽  
A. PETRI ◽  
A. ALIPPI ◽  
G. PAPARO ◽  
M. COSTANTINI
Keyword(s):  
Time Series ◽  
Acoustic Emission ◽  
Statistical Analysis ◽  
Power Spectrum ◽  
Power Law ◽  
Amplitude Distribution ◽  
Relaxation Processes ◽  
Critical Dynamics ◽  
Range Correlation ◽  
Correlation Properties

Relaxation processes taking place after microfracturing of laboratory samples give rise to ultrasonic acoustic emission signals. Statistical analysis of the resulting time series has revealed many features which are characteristic of critical phenomena. In particular, the autocorrelation functions obey a power-law behavior, implying a power spectrum of the kind 1/f. Also the amplitude distribution N(V) of such signals follows a power law, and the obtained exponents are consistent with those found in other experiments: N(V) dV≃V–γ dV, with γ=1.7±0.2. We also analyzed the distribution N(τ) of the delay time τ between two consecutive acoustic emission events. We found that a N(τ) distribution rather close to a power law constitutes a common feature of all the recorded signals. These experimental results can be considered as a striking evidence for a critical dynamics underlying the microfracturing processes.

Study of Oil Starvation in Journal Bearing Using Acoustic Emission and Vibration Measurement Techniques

2020 ◽  
Vol 142 (12) ◽  
Author(s):  
Surojit Poddar ◽  
N. Tandon
Keyword(s):  
Acoustic Emission ◽  
Journal Bearing ◽  
Measurement Techniques ◽  
Peak Frequency ◽  
Vibration Measurement ◽  
Frequency Range ◽  
Frequency Shifts ◽  
Deformation And Fracture ◽  
Asperity Contacts ◽  
Ae Signals

Abstract This present article evaluates the state of starvation in a journal bearing using acoustic emission (AE) and vibration measurement techniques. A journal bearing requires a constant supply of oil in an adequate amount to develop a hydrodynamic film, thick enough to separate the surfaces and avoid asperity contacts. On a microscopic level, the surface interaction under starved lubrication results in deformation and fracture of asperities. This causes a proportionate increase in AE and vibration. The AE activities resulting from asperities interaction have significant energy in the frequency range of 100–400 kHz with peak frequencies in the range of 224–283 kHz. Further, the peak frequency shifts from the higher to lower side as the asperity interaction transits from the elastic to plastic contact. This information derived from the spectral analysis of AE signals can be used to develop condition monitoring parameters to proactively control the lubrication and prevent bearing failure.

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