scholarly journals Acoustic Emission Experimental Research of the Damage Characteristics of Raw Coal under Different Loading and Unloading Rates

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Minbo Zhang ◽  
Li Cui ◽  
Wenjun Hu ◽  
Jinlei Du ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Practical Significance ◽  
Emission Characteristic ◽  
Strain Rates ◽  
Characteristic Parameters ◽  
Energy Increase ◽  
Damage Models ◽  
Ae Energy ◽  
Ae Counts ◽  
Cumulative Ae Energy

In this study, triaxial load failure experiments of coal samples under different strain rates and different confining pressure unloading rates were carried out using an RTX-1000 rock triaxial apparatus, and the acoustic emission characteristic parameters of a Micro-II acoustic emission imaging acquisition instrument were used to study the acoustic emission characteristics and damage deformation law of coals under different conditions. Damage models were constructed on the basis of the characteristic parameters to analyze the damage law of coal samples. Experimental results show that the acoustic emission (AE) counts and AE energy of the coal samples decrease, but the peak AE counts and peak AE energy increase with the increase in strain rates. The cumulative AE counts decrease from 9902 times to 6899 times, the peak counts increase from 209 times to 431 times, the cumulative AE energy decreases from 6986 aJ to 3786 aJ, and the peak AE energy increases from 129 aJ to 312 aJ. The overall level of the AE count rates and the AE energy of the coal samples decrease, but the peak AE counts and peak AE energy increase with the increase in unloading rates. The cumulative AE counts decrease from 18,689 times to 16,842 times, the peak AE count rates increase from 245 times/s to 535 times/s, the cumulative AE energy decreases from 9846 aJ to 7430 aJ, and the peak energy increases from 257 aJ to 587 aJ. The damage models are constructed on the basis of AE counts, and the comparative experimental and theoretical curves are analyzed to obtain a higher fitness close to 1. The damage threshold increases from 0.30 to 0.50 and from 0.34 to 0.55, and the damage amount increases from 0.50 to 0.60 and from 0.34 to 0.62 with the increase in strain rates and unloading rates. The research results have practical significance for revealing the mechanism of disaster occurrence in actual engineering excavation and proposing engineering measures to prevent coal rock damage and disaster occurrence.

scholarly journals Scale Effect on the Anisotropy of Acoustic Emission in Coal

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Honghua Song ◽  
Yixin Zhao ◽  
Yaodong Jiang ◽  
Jiehao Wang
Keyword(s):  
Acoustic Emission ◽  
Energy Dissipation ◽  
Scale Effect ◽  
Specimen Size ◽  
Exponential Equation ◽  
Volume Number ◽  
Ae Energy ◽  
Negative Exponential ◽  
Ae Counts ◽  
Cumulative Ae Energy

Acoustic emission (AE) in coal is anisotropic. In this paper, we investigate the microstructure-related scale effect on the anisotropic AE feature in coal at unconfined loading process. A series of coal specimens were processed with diameters of 25 mm, 38 mm, 50 mm, and 75 mm (height to diameter ratio of 2) and anisotropic angles of 0°, 15°, 30°, 45°, 60°, and 90°. The cumulative AE counts and energy dissipation increase with the specimen size, while the energy dissipation per AE count behaves in the opposite way. This may result from the increasing amount of both preexisting discontinuities and cracks (volume/number) needed for specimen failure and the lower energy dissipation AE counts generated by them. The effect of microstructures on the anisotropies of AE weakens with the increasing specimen size. The TRFD and its anisotropy reduce as the specimen size increases, and the reduction of fractal dimension is most pronounced at the anisotropic angle of 45°. The correlation between TRFD and cumulative AE energy in the specimens with different sizes are separately consistent with the negative exponential equation proposed by Xie and Pariseau. With the specimen size gain, the reduction of the TRFD weakens with the increasing amount of cumulative absolute AE energy.

scholarly journals Multivariate Prediction Model of Strength and Acoustic Emission Energy considering Parameter Correlation of Coal or Rock

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Shuncai Li ◽  
Daquan Li ◽  
Nong Zhang
Keyword(s):  
Compressive Strength ◽  
Acoustic Emission ◽  
Coal Sample ◽  
Regression Models ◽  
Loading Rate ◽  
Mass Density ◽  
Acoustic Velocity ◽  
Characteristic Parameters ◽  
Ae Energy ◽  
Coal Rock

Due to the heterogeneity of the internal structure and the different external loading conditions, the mechanical and acoustic emission (AE) characteristic parameters of coal and rock are discrete in the process of loading until failure, and many repeated and destructive tests need to be completed to obtain the performance parameters. It is of theoretical significance to explore the correlation of various parameters and to establish multiparameter regression models of coal rock strength and AE characteristics for predicting the strength and acoustic emission characteristic parameters of coal rock and reducing the repeated tests. For the coal sample from a coal seam of Longde Coal Mine in China, the mass density of coal samples and the acoustic velocity in the samples before loading are measured at first, and their respective coefficient of variation is analyzed. Then, the stress-strain curve and the time history curve of AE characteristic parameters are obtained by the uniaxial compression AE test of each coal sample according to the different loading rates. The influence of loading rate, mass density, and acoustic velocity on the mechanical and AE energy parameters of coal sample is analyzed by the section morphology of the coal sample after failure, the three-dimensional location map of AE, and the scanning micrograph of the electron microscope. Based on the least-square method, the multiple regression models of compressive strength, elastic modulus, and the maximum AE energy are established by mass density, acoustic velocity, and loading rate of coal samples. The results indicate that, for the coal samples from the same geological source, the obtained regression models can, respectively, predict the uniaxial compressive strength, elastic modulus, and the maximum AE energy according to the predesigned loading rate, the acoustic velocity, and mass density of coal samples measured before loading, without too many repeated loading failure tests.

scholarly journals Tensile Properties Analysis Of 3D Flat-Knitted Inlay Fabric Reinforced Composites Using Acoustic Emission

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Jiangtao Tan ◽  
Gaoming Jiang ◽  
Guangjun Wu ◽  
Pibo Ma
Keyword(s):  
Acoustic Emission ◽  
Tensile Properties ◽  
Reinforced Composites ◽  
Fiber Breakage ◽  
Damage Mechanisms ◽  
Duration Time ◽  
Ae Energy ◽  
Matrix Fracture ◽  
Interlock Structure ◽  
Cumulative Ae Energy

Abstract In this work, based on the quasi-static tensile test and acoustic emission technology, the tensile properties of two types of three-dimensional flat-knitted inlay fabrics reinforced composites are investigated, and the acoustic emission characteristic parameters of various damage mechanisms are obtained. The transverse tensile process of specimens could be divided into the elastic stage, yield stage, and fracture stage. We found that, compared with the fluctuation of the stress-strain curve in the yield stage, weft insertion yarns in composite with interlock structure broke almost simultaneously, while the composite with plain stitch broke successively. The transverse and longitudinal tensile strength of the composite with interlock structure was 44.70% and 28.63% higher than the composite with plain structure, respectively. The SEM micrographs showed that the damage mechanism of the composites was matrix fracture, fiber-matrix debonding, and fiber breakage. The amplitude ranges of the three damage mechanisms were 50–65 dB, 65–80 dB, and 90–100 dB, respectively, and the frequency ranges were 35–114 kHz, 116–187 kHz, and 252–281 kHz, respectively. Fiber-matrix debonding and matrix fracture had large cumulative AE energy, numerous events, and long duration time, while fiber breakage had the characteristics of large amplitude, high frequency, low cumulative AE energy, few events, and short duration time.

Research on damage evolution of deep formation rock based on acoustic emission test

2020 ◽  
Vol 30 (1) ◽  
pp. 145-159
Author(s):  
Yong Tian ◽  
Rangang Yu ◽  
Yin Zhang ◽  
Xinbo Zhao
Keyword(s):  
Acoustic Emission ◽  
Cyclic Loading ◽  
Damage Evolution ◽  
Initial Moment ◽  
Damage Development ◽  
Loading Test ◽  
Cyclic Loading Test ◽  
Ae Energy ◽  
Rock Specimens ◽  
Cumulative Ae Energy

The study of rock damage evolution is of great significance in the field of underground engineering. In this paper, the damage development of deep formation rock was quantitatively evaluated by acoustic emission (AE) test. The Young’s modulus of the test rock specimens under ideal intact state was obtained by assuming a linear relationship between the AE rate parameter and the damage variation based on the rate process theory. Through the multi-stage cyclic loading test, the damage parameters corresponding to the peak stress of the previous stage were calculated by using the tangent modulus at the initial moment. The results showed that there was abrupt transition stage of damage development with the linear increase of stress. The damage parameter curves of rock specimens during loading process were obtained by using the method of cumulative AE energy, and the development trend of the curves was analyzed simply by combining the concepts of crack initiation stress and crack damage stress. Comparing the two methods of obtaining damage parameters by using cyclic loading test and cumulative AE energy, the results of them were highly consistent except for some deviation in the initial and final stages.

Rock’s Acoustic Emission and Damage Deformation Characteristic in Uniaxial Compression Condition

2012 ◽  
Vol 170-173 ◽  
pp. 486-491 ◽  
Author(s):  
Zi Hong Guo ◽  
Qian Tao ◽  
Bao Xian Liu ◽  
Chao Luo ◽  
Li Meng ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Uniaxial Compression ◽  
Deformation Characteristic ◽  
Constitutive Models ◽  
Emission Characteristic ◽  
Uniaxial Compression Test ◽  
Strength Based ◽  
Damage Constitutive Model ◽  
Set Up ◽  
Ae Counts

Rock’s uniaxial compression test is completed and the datum is analyzed, then relationship between loading time and AE amplitude, AE counts sum, AE total energy’s released rate come out. Rock’s critical damage is defined which equal result of one minus ratios of residual strength to critical strength. Based on strain equivalence discipline, damage constitutive models related with acoustic emission characteristic are set up. With damage constitutive models established with AE counts sum and AE total energy serving as damage variables respectively and testing datum, the relationship between rock’s damage and stress and strain is described. Then conclusions come out: 1) damage constitutive model with AE counts sum serving as damage variable manifest rock’s damage increasing process and accords with testing result better; 2) theoretical curve is well consistent with testing curve at rock’s peak strength zone.

Crack Position Recognition of Metal Deep Drawing Parts Based on BP Neural Network

2013 ◽  
Vol 318 ◽  
pp. 108-113
Author(s):  
Ji Yong Xu ◽  
Jun Li Zhao ◽  
Bing Zhao ◽  
Ying Qing Shao
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Bp Neural Network ◽  
Deep Drawing ◽  
Emission Characteristic ◽  
Characteristic Parameters ◽  
Forming Process ◽  
Crack Location ◽  
Location Identification ◽  
Crack Position

Crack position of metal drawing parts molding was analyzed by the BP neural network. First analysis of the drawing parts forming process may crack in different position. The BP neural network location identification was introduced in the basic process. 11 characteristic parameters from the drawing parts may crack position were gathered by acoustic emission signal acquisition system of deep drawing process. Then the BP neural network was designed rational, and carried out appropriate conduct to train and test. Establishing deep drawing parts of the relations between the different positions crack acoustic emission characteristic parameters and the corresponding position. Crack location was identified, in order to achieve the purpose of positioning the work piece forming process. The better method of acoustic emission location issues are resolved, metal deep drawing forming of crack location identification for basis. Provide the basis for metal drawing parts forming crack location identification.

scholarly journals Bearing defect diagnosis and acoustic emission

2003 ◽  
Vol 217 (4) ◽  
pp. 257-272 ◽  
Author(s):  
A Morhain ◽  
D Mba
Keyword(s):  
Acoustic Emission ◽  
Threshold Level ◽  
Diagnostic Methods ◽  
Non Destructive Testing ◽  
Test Rig ◽  
Destructive Testing ◽  
High Background ◽  
Bearing Defect ◽  
Non Destructive ◽  
Ae Counts

Acoustic emission (AE) was originally developed for non-destructive testing of static structures, but over the years its application has been extended to health monitoring of rotating machines and bearings. It offers the advantage of earlier defect detection in comparison with vibration analysis. However, limitations in the successful application of the AE technique for monitoring bearings have been partly due to the difficulty in processing, interpreting and classifying the acquired data. The investigation reported in this paper was centred on the application of standard AE characteristic parameters on a radially loaded bearing. An experimental test rig was modified such that defects could be seeded onto the inner and outer races of a test bearing. As the test rig was adapted for this purpose, it offered high background acoustic emission noise providing a realistic test for fault diagnosis. In addition to a review of current diagnostic methods for applying AE to bearing diagnosis, the results of this investigation validated the use of r. m. s., amplitude, energy and AE counts for diagnosis. Furthermore, this study determined the most appropriate threshold level for AE count diagnosis, the first known attempt.

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