Fracture acoustic emission signals identification of broken wire using deep transfer learning and wavelet analysis

In this paper, there are two kinds of material (cermet and cemented carbide) to be used. Different grinding conditions were performed for evaluation of RMS of acoustic emission(AE) signals and understanding the effect of each grinding parameter on AE RMS during grinding process. A kind of on-line monitoring method based on wavelet analysis and acoustic emission was raised and the nonlinear relation model between AE RMS,wavelet energy coefficients and wheel passivation state was built. During the process,the wavelet analysis method was used to decompose the original signals for extracting wavelet energy coefficients. The results of experiments indicates that AE RMS increases with increasing table speed;The corresponding relation between AE RMS and table speed is good and could take the table speed as the main parameter for studying wheel passivation state. As a result, the nonlinear relation model can monitor the wheel passivation degree on-line accurately through training. This provides a kind of viable method which has very high practical value for confirming the wheel dressing cycle.

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Results of the wavelet analysis of the acoustic emission signals generated by partial discharges in insulation oil of various temperature and flow speed

The European Physical Journal Special Topics ◽

10.1140/epjst/e2008-00513-6 ◽

2008 ◽

Vol 154 (1) ◽

pp. 31-38 ◽

Cited By ~ 1

Author(s):

A. Cichon ◽

S. Borucki ◽

T. Boczar ◽

M. Lorenc

Keyword(s):

Acoustic Emission ◽

Wavelet Analysis ◽

Flow Speed ◽

Partial Discharges ◽

Insulation Oil

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Research and Analysis on Rock Acoustic Emission Wave Characteristics

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.638-640.534 ◽

2014 ◽

Vol 638-640 ◽

pp. 534-537

Author(s):

Jian Ping He

Keyword(s):

Acoustic Emission ◽

Wavelet Analysis ◽

Laboratory Tests ◽

Low Frequency ◽

Development Trend ◽

Signal Wave ◽

Original Signal ◽

Great Efficiency ◽

Wave Characteristics ◽

Ae Signal

Analyzing wave behavior on acoustic emission laboratory tests from relationship between stress and AE rate , the rock AE signal wave in laboratory is decomposed into high and low frequency elements. Analysis and compare with the details elements include, the approximation elements and original AE wave, the results show that rock AE wave characteristics are not same in the course of transform fracture on stages, found AE wave characteristics storehouse, it is a matter of great significance for utilizing monitoring and prediction regularity and development trend in the course of fracture transform. The error between the original signal and the signal of wave coefficient of the approximation elements reconstructed is minor to, wavelet analysis makes accuracy and reliability of rock AE wave characteristics monitoring and prediction improved, States that wavelet analysis is a great efficiency method.

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Wavelet analysis of acoustic emission signals and quality control in laser welding

Journal of Laser Applications ◽

10.2351/1.1386799 ◽

2001 ◽

Vol 13 (4) ◽

pp. 167-173 ◽

Cited By ~ 19

Author(s):

Hao Zeng ◽

Zude Zhou ◽

Youping Chen ◽

Hong Luo ◽

Lunji Hu

Keyword(s):

Quality Control ◽

Acoustic Emission ◽

Wavelet Analysis ◽

Laser Welding

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STUDY ON PROPAGATION LAW OF ACOUSTIC EMISSION SIGNALS ON ANISOTROPIC WOOD SURFACE

Wood Research ◽

10.37763/wr.1336-4561/66.4.517527 ◽

2021 ◽

Vol 66 (4) ◽

pp. 517-527

Author(s):

TINGTING DENG ◽

SHUANG JU ◽

MINGHUA WANG ◽

MING LI

Keyword(s):

Acoustic Emission ◽

Wavelet Analysis ◽

Wood Surface ◽

Propagation Velocity ◽

Quadratic Function ◽

Propagation Direction ◽

Mode Decomposition ◽

Propagation Law ◽

Ae Signals ◽

Obvious Feature

In order to explore the influence of wood’s anisotropic characteristics on Acoustic Emission (AE) signals’ propagation, the law of AE signals’ propagation velocity along different directions was studied. First, The center of the specimen’s surface was took as the AE source,then 24 directions were chose one by one every 15º around the center,and 2 AE sensors were arranged in each direction to collect the original AE signals. Second, the wavelet analysis was used to denoise the original AE signals, then the AE signals were reconstructedby Empirical Mode Decomposition (EMD). Finally, time difference location method was utilized to calculate AE signals’ propagation velocity. The results demonstrate that AE signals’ propagation velocity has obvious feature of quadratic function. In the range of 90º, as the angle of propagation direction increases, the propagation velocity of the AE signals presents a downward trend.

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