scholarly journals Time–Frequency Domain Characteristics of Acoustic Emission Signals and Critical Fracture Precursor Signals in the Deep Granite Deformation Process

2021 ◽  
Vol 11 (17) ◽  
pp. 8236
Author(s):  
Le Zhang ◽  
Hongguang Ji ◽  
Liyuan Liu ◽  
Jiwei Zhao
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
High Frequency ◽  
Wavelet Packet ◽  
Confining Pressure ◽  
Wavelet Packet Decomposition ◽  
Evolution Law ◽  
Time Frequency ◽  
Characteristic Signal ◽  
Failure Precursor

To study the crack evolution law and failure precursory characteristics of deep granite rocks in the process of deformation and failure under high confining pressure, granite samples obtained from a depth of 1150 m are tested using a TAW-2000 triaxial hydraulic servo testing machine and a PCI-II acoustic emission monitoring system. Based on the stress–strain curve and IET function, the loading process of the sample is divided into five stages: crack closure, linear elastic deformation, microcrack generation and development, macroscopic fracture generation and energy surge, and post-peak failure. The evolution trend and fracture evolution law of the acoustic emission signal event interval function in different stages are analyzed. In particular, the signals with an amplitude greater than 85 dB, a peak frequency greater than 350 kHz, and a frequency centroid greater than 275 kHz are defined as the failure precursor signals before the rock reaches the peak stress. The defined precursor signal conditions agree well with the experimental results. The time–frequency analysis and wavelet packet decomposition of the precursor signal are performed on the extracted characteristic signal of the failure precursor. The results show that the time-domain signal is in the form of a continuous waveform, and the frequency-domain waveform has multi-peak coexistence that is mainly concentrated in the high-frequency region. The energy distribution obtained by the wavelet packet decomposition of the characteristic signal is verified with the frequency-domain waveform. The energy distribution of the signal is mainly concentrated in the 343.75–375 kHz frequency band, followed by the 281.25–312.5 kHz frequency band. The energy proportion of the high-frequency signal increases with the confining pressure.

Intelligent Fault Diagnosis of Rolling Element Bearing Based on SVMs and Statistical Characteristics

2007 ◽  
Author(s):  
Junyan Yang ◽  
Youyun Zhang ◽  
Yongsheng Zhu ◽  
Qinghua Wang
Keyword(s):  
Frequency Domain ◽  
Wavelet Packet ◽  
Classification Performance ◽  
Rolling Element Bearing ◽  
Statistical Characteristics ◽  
Wavelet Packet Decomposition ◽  
Frequency Bands ◽  
Time Frequency ◽  
Rolling Element ◽  
Element Bearing

In this paper, the statistical characteristics of time, frequency and time-frequency domain are applied to discriminate various fault types and evaluate various fault conditions of rolling element bearing, and the classification performance of them is evaluated by using SVMs. Experimental results showed that the statistical characteristics Mean, Variance, Root, RMS and Peak of the 25 sub frequency bands in frequency domain obtain higher classification accuracy rate on all the fault datasets than the statistical characteristics in the whole time and frequency domain. Wavelet packet decomposition is an efficient time-frequency analysis tool, and it can decompose the original signal into independent frequency bands. Experiment on the statistical characteristics of the 5th level wavelet packet decomposition showed that the statistical characteristics Variance, Root, RMS and Peak can discriminate various fault types and evaluate various fault conditions well on all the datasets. Compared with the statistical characteristics of sub frequency bands in frequency domain, the classification performance of the statistical characteristics of the wavelet packet transform is a little lower than that of the statistical characteristics of sub frequency bands in frequency domain.

scholarly journals An Investigation into Error Source Identification of Machine Tools Based on Time-Frequency Feature Extraction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Dongju Chen ◽  
Shuai Zhou ◽  
Lihua Dong ◽  
Jinwei Fan
Keyword(s):  
Machine Tool ◽  
Frequency Domain ◽  
Surface Topography ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Time Frequency ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method

This paper presents a new identification method to identify the main errors of the machine tool in time-frequency domain. The low- and high-frequency signals of the workpiece surface are decomposed based on the Daubechies wavelet transform. With power spectral density analysis, the main features of the high-frequency signal corresponding to the imbalance of the spindle system are extracted from the surface topography of the workpiece in the frequency domain. With the cross-correlation analysis method, the relationship between the guideway error of the machine tool and the low-frequency signal of the surface topography is calculated in the time domain.

Tool Wear Monitoring in Milling Processes Based on Time-Frequency Analysis of Acoustic Emission

2011 ◽  
Vol 141 ◽  
pp. 574-577
Author(s):  
Lu Zhang ◽  
Guo Feng Wang ◽  
Xu Da Qin ◽  
Xiao Liang Feng
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Frequency Domain ◽  
Support Vector ◽  
Tool Wear Monitoring ◽  
Machining Processes ◽  
Time Frequency ◽  
Svm Model ◽  
The Status ◽  
Wear Monitoring

Tool wear monitoring plays an important role in the automatic machining processes. Therefore, it is necessary to establish a reliable method to predict tool wear status. In this paper, features of acoustic emission (AE) extracted from time-frequency domain are integrated with force features to indicate the status of tool wear. Meanwhile, a support vector machine (SVM) model is employed to distinguish the tool wear status. The result of the classification of different tool wear status proved that features extracted from time-frequency domain can be the recognize-features of high recognition precision.

Intelligent Identification of Flow Regime Based on a Novel Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 1715-1718
Author(s):  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Flow Regime ◽  
Recurrent Neural Network ◽  
Pressure Difference ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Elman Neural Network ◽  
Flow Regime Identification ◽  
Novel Method

A noveol neural network of Elman is typically dynamic recurrent neural network. A novel method of flow regime identification based on Elman neural network and wavelet packet decomposition is proposed in this paper. Above all, the collected pressure-difference fluctuation signals are decomposed by the four-layer wavelet packet, and the decomposed signals in various frequency bands are obtained within the frequency domain. Then the wavelet packet energy eigenvectors of flow regimes are established. At last the wavelet packet energy eigenvectors are input into Elman neural network and flow regime intelligent identification can be performed.

scholarly journals Application of the Acoustic Emission Method for Diagnosis of On-Load Tap Changer

2017 ◽  
Vol 42 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Henryk Majchrzak ◽  
Andrzej Cichoń ◽  
Sebastian Borucki
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Operating Conditions ◽  
Time Frequency ◽  
Measurement Results ◽  
Tap Changer ◽  
The Time Domain ◽  
Load Tap Changer ◽  
Short Time

Abstract This paper provides an example of the application of the acoustic emission (AE) method for the diagnosis of technical conditions of a three-phase on-load tap-changer (OLTC) GIII type. The measurements were performed for an amount of 10 items of OLTCs, installed in power transformers with a capacity of 250 MVA. The study was conducted in two different OLTC operating conditions during the tapping process: under load and free running conditions. The analysis of the measurement results was made in both time domain and time-frequency domain. The description of the AE signals generated by the OLTC in the time domain was performed using the analysis of waveforms and determined characteristic times. Within the time-frequency domain the measured signals were described by short-time Fourier transform spectrograms.

The Improvement of Wavelet Packet for Psychoacoustic Model

2012 ◽  
Vol 591-593 ◽  
pp. 2491-2494
Author(s):  
Jing Lei Zhou ◽  
Ying Li
Keyword(s):  
Spectrum Analysis ◽  
Wavelet Packet ◽  
Critical Band ◽  
Frequency Resolution ◽  
Wavelet Packet Decomposition ◽  
Frequency Division ◽  
Time Frequency ◽  
Psychoacoustic Model ◽  
Insufficient Time ◽  
The Way

Compared with common psychoacoustic model, this article uses wavelet packet decomposition to decompose a signal. This method improves the situation of insufficient time-frequency resolution which the uniform spectrum analysis causes. In addition, frequency division by wavelet packet decomposition is much closer to human’s critical band than the way common psychoacoustic model getting, it is more suitable to human’s hearing characteristics. So we can use wavelet packet decomposition replace FFT in MPEG, and get accurate psychoacoustic model.

Reflection technique in time‐frequency domain using multicomponent acoustic emission signals and application to geothermal reservoirs

Geophysics ◽  
2002 ◽  
Vol 67 (3) ◽  
pp. 928-938 ◽  
Author(s):  
Nobukazu Soma ◽  
Hiroaki Niitsuma ◽  
Roy Baria
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Random Noise ◽  
Spherical Shape ◽  
Source Distribution ◽  
S Wave ◽  
Spectral Matrix ◽  
Time Frequency ◽  
Reflected Wave

We have developed a reflection technique for estimating deep geothermal reservoir structures using acoustic emission signals as a source, which is useful when there is no proper estimating technique because of high temperature, high pressure, and great depth. Because its resolution is not high enough for comparison with methods such as well logging, we have enhanced the technique by developing a time–frequency‐domain analysis of multicomponent acoustic emission signals using a wavelet transform. The reflected wave is separated from an incoherent coda by analyzing the shape of a 3‐D hodogram: a linear shape indicates the arrival of a coherent signal such as a reflected wave, and an incoherent signal such as a coda makes a spherical shape. We construct a spectral matrix of 3‐D particle motion using a wavelet transform, as is done in a time–frequency domain. We evaluate the linearity of the 3‐D hodogram for each time and frequency by using the eigenvalues of the spectral matrix. Three‐dimensional inversion of the distribution of hodogram linearity in the time–frequency domain lets us image the deep subsurface structure. The inversion is based on the diffraction stack. We reduce the uncertainties by investigating S‐wave polarization direction, and we compensate for inhomogeneous source distribution to get reliable estimates with high resolution. We then evaluate our methods with synthetic signals. We discriminate a coherent wave from incoherent random noise in the presence of an S/N ratio of −3.7 dB and detect reflectors at correct depths with a small number of detectors. We apply the method to data from the European hot, dry rock site in Soultz‐sous‐Forêts, France, and compare our estimates with those from a number of borehole observations. The detected reflectors agree with the location of fracture zones. We demonstrate the feasibility of the method for detecting reflectors at great depths.

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