scholarly journals Identification of crack development in granite under triaxial compression based on the acoustic emission signal

2021 ◽  
Vol 17 (1) ◽  
pp. 155014772098611 ◽  
Author(s):  
Tianzuo Wang ◽  
Linxiang Wang ◽  
Fei Xue ◽  
Mengya Xue
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Acoustic Emission Signal ◽  
Triaxial Compression ◽  
B Value ◽  
Quiet Period ◽  
Active Stage ◽  
Emission Signal ◽  
The Time Domain ◽  
Granite Samples

To explore the development mechanism of cracks in the process of rock failure, triaxial compression tests with simultaneous acoustic emission monitoring were performed on granite specimens using the MTS rock mechanics test system. The frequency-domain information of the acoustic emission signal was obtained by the fast Fourier transform. The Gutenberg–Richter law was used to calculate the acoustic emission signals and obtain the b-value dynamic curve in the loading process. Combined with the stiffness curve of granite specimens and acoustic emission signal in the time domain and frequency domain, the crack development characteristics in different stages were analyzed. The results showed that the acoustic emission signals of granite samples under triaxial compression can be divided into four stages: quiet period 1, active stage 1, quiet period 2, and active stage 2. b-value attained its maximum value in the active phase 2 when it is close to the sample loss, and then drops rapidly, which means the propagation of cracks and the formation of large cracks. The acoustic emission signal’s dominant frequency was not more than 500 kHz, mostly concentrated in the medium-frequency band (100–200 kHz), which accounted for more than 80%. The proportion of signals in each frequency band can reflect the distribution of the three kinds of cracks, while the change in low-frequency signals can reflect the breakthrough of microcracks and the formation time of macrocracks in granite samples. By fully analyzing the characteristics of acoustic emission signals in the time domain and frequency domain, the time and conditions of producing large cracks can be determined accurately and efficiently.

Acoustic Emission Signal Classification Using Feature Analysis and Deep Learning Neural Network

2020 ◽  
pp. 2150030
Author(s):  
Jian-Da Wu ◽  
Yu-Han Wong ◽  
Wen-Jun Luo ◽  
Kai-Chao Yao
Keyword(s):  
Acoustic Emission ◽  
Deep Learning ◽  
Frequency Domain ◽  
Acoustic Emission Signal ◽  
Signal Classification ◽  
Learning Method ◽  
Identification Rate ◽  
Emission Signal ◽  
Identification Time ◽  
The Impact

With the development of artificial intelligence in recent years, deep learning has been widely used in mechanical system signal classification but the impact of different feature extractions on the efficiency and effectiveness of deep learning neural networks is more important. In this study, a vehicle classification based on engine acoustic emission signal in the time domain, the frequency domain and the wavelet transform domain for deep learning network techniques is presented and compared. In signal classification, different feature extractions will show in different decomposition levels and can be used to recognize the various acoustic conditions. In the experimental work, as engines from 10 different ground vehicles operate, the measured sound signal is converted into a digital signal, and the established data set is classified and identified by the deep learning method. The number of samples, identification rate and identification time in the various signal domains are compared and discussed in this study. Finally, the experimental results and data analysis show that by using the wavelet signal and the deep learning method, excellent identification time and identification rate can be achieved, compared with traditional time and frequency domain signals.

Evaluating the sensitivity of acoustic emission signal features to the variation of cutting parameters in milling aluminum alloys: Part A: frequency domain analysis

2020 ◽  
pp. 095440542094912
Author(s):  
Mohamad Javad Anahid ◽  
Hoda Heydarnia ◽  
Seyed Ali Niknam ◽  
Hedayeh Mehmanparast
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Acoustic Emission Signal ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Emission Signal ◽  
Signal Parameters ◽  
Signal Features

It is known that adequate knowledge of the sensitivity of acoustic emission signal parameters to various experimental parameters is indispensable. According to the review of the literature, a lack of knowledge was noticeable concerning the behavior of acoustic emission parameters under a broad range of machining parameters. This becomes more visible in milling operations that include sophisticated chip formation morphology and significant interaction effects and directional pressures and forces. To remedy the aforementioned lack of knowledge, the effect of the variation of cutting parameters on the time and frequency features of acoustic emission signals, extracted and computed from the milling operation, needs to be investigated in a wide aspect. The objective of this study is to investigate the effects of cutting parameters including the feed rate, cutting speed, depth of cut, material properties, as well as cutting tool coating/insert nose radius on computed acoustic emission signals featured in the frequency domain. Similar studies on time-domain signal features were already conducted. To conduct appropriate signal processing and feature extraction, a signal segmentation and processing approach is proposed based on dividing the recorded acoustic emission signals into three sections with specific signal durations associated with cutting tool movement within the work part. To define the sensitive acoustic emission parameters to the variation of cutting parameters, advanced signal processing and statistical approaches were used. Despite the time features of acoustic emission signals, frequency domain acoustic emission parameters seem to be insensitive to the variation of cutting parameters. Moreover, cutting factors governing the effectiveness of acoustic emission signal parameters are hinted. Among these, the cutting speed and feed rate seem to have the most noticeable effects on the variation of time–frequency domain acoustic emission signal information, respectively. The outcomes of this work, along with recently completed works in the time domain, can be integrated into advanced classification and artificial intelligence approaches for numerous applications, including real-time machining process monitoring.

scholarly journals Identifying critical failure information of thermal damaged sandstone through acoustic emission signal

2021 ◽  
Vol 18 (4) ◽  
pp. 558-566
Author(s):  
Weiqiang Zhang ◽  
Zhoujian Shi ◽  
Zuoquan Wang ◽  
Shaoteng Zhang
Keyword(s):  
Acoustic Emission ◽  
Critical Point ◽  
High Temperatures ◽  
Acoustic Emission Signal ◽  
Large Scale ◽  
B Value ◽  
Energy Curve ◽  
Cumulative Energy ◽  
Temperature Range ◽  
Emission Signal

Abstract The changes in the acoustic emission signals of sandstone after treatment at different high temperatures are examined in this study. The results show that there is a critical point on the cumulative energy curve of the acoustic emission signals (almost between 60 and 90% of the ratio of the loading time and the total loading time), which can be used to identify the failure of sandstone that has been damaged by exposure to a temperature of 900°C. As the temperature increases, the position of the critical point gradually changes, which indicates that high temperatures increase the plasticity of rock, and this gradually reduces the brittleness. The changes in b-value of acoustic emission shows that the transition behavior of rock from brittleness to plasticity is more obvious at temperatures higher than 600°C, and the large-scale micro cracking takes place at that temperature range, which is the main reason for the weakening and brittleness and the strengthening of plasticity of the sandstone.

Signatures of Acoustic Emission Signals Generated During High Speed Cutting

2001 ◽  
Author(s):  
Andre Sitz ◽  
Udo Schwarz ◽  
J. Kurths ◽  
Doris Maus ◽  
Michael Wiese ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Data Analysis ◽  
Differential Equations ◽  
Frequency Domain ◽  
Acoustic Emission Signal ◽  
High Speed ◽  
Cutting Process ◽  
High Speed Cutting ◽  
Emission Signal ◽  
Linear And Nonlinear

Abstract Acoustic emission signals generated during high speed cutting of steel are investigated. The data are represented in time-folded form. Several methods from linear and nonlinear data analysis based on time- and frequency-domain are applied to the data and reveal signatures of the observed acoustic emission signal. These investigations are first steps for modeling the cutting process by means of differential equations.

Identifying the Crack Nature Using b-Value Acoustic Emission Signal Analysis

2019 ◽  
pp. 1065-1076
Author(s):  
N. A. A. S. Bahari ◽  
Shahiron Shahidan ◽  
M. F. M. Shukri ◽  
Sharifah Salwa Mohd Zuki ◽  
M. Y. Norbazlan ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Signal Analysis ◽  
B Value ◽  
Emission Signal

Discussion on the Relationship between Rockburst Proneness of Rock and its AE b-Value

2011 ◽  
Vol 137 ◽  
pp. 398-402
Author(s):  
Guang Zhang ◽  
Mo Xiao Li ◽  
Jing Xi Chen
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Peak Stress ◽  
B Value ◽  
Uniaxial Compression Test ◽  
B Values ◽  
Emission Signal ◽  
Whole Process ◽  
In The Beginning ◽  
The Relationship

In order to strengthen the study on rockburst prediction and inquire the relationship between rockburst proneness of rock and its AE b-value, we select three typical rocks of volcanic, sedimentary, and metamorphic to conduct indoor rock mechanics test. Uniaxial compression test are carried to calculate the rockburst proneness of three kinds of rocks, at the meantime we collect the acoustic emission signal during the whole process by acoustic emission instrument. After analyzing the different AE features of all kinds of rocks, we find that the AE b-values of three kinds of rocks both develop in the beginning of two different loading conditions. The AE b-values of marble and sandstone change in a more stable way, and the b-value of granite decline at the 50% of the peak stress. The b-value of granite is the biggest and the sandstone’s is the smallest.

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

Sign in / Sign up

Export Citation Format

Share Document