Wavelet function suitable for fault feature extraction of acoustic emission signal

2008 ◽  
Vol 44 (03) ◽  
pp. 177
Author(s):  
Xuejun LI
2017 ◽  
Vol 25 (4) ◽  
pp. 934-942
Author(s):  
毕果 BI Guo ◽  
许涛林 XU Tao-lin ◽  
彭云峰 Peng Yun-feng ◽  
郭昕乾 GUO Xin-qian

2011 ◽  
Vol 488-489 ◽  
pp. 432-435
Author(s):  
Qi Wang ◽  
Yin Sheng Chen ◽  
Kai Song

The appearance and growth of the microcracks in the structure is an important factor that influences the structure safety and its service life. Thus it is very important to detect the crack and monitor its growth at the beginning of the crack. Aiming at the main style of failures in metal structure - fatigue fracture, this paper research acoustic emission waveforms analysis that base on wavelet packets feature extraction, through processing acoustic emission signal to test metal fatigue fracture. First, this paper analyses the reason of metal fatigue fracture and introduces the theory of acoustic emission. Based on that, we establish the time domain module of acoustic emission signal and extract the feature of acoustic emission signal using wavelet packets. According to the experimental results bending specimen, acoustic emission techniques monitoring fatigue crack propagation is certificated not only to resemble variable rule of fatigue crack propagation but also to catch generation of fatigue crack in real time. Compared with the method of parameter extraction, this method can not only realize real-time and dynamic monitoring, but also get the result that is similar with fatigue crack expanding rate curve.


2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin

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]


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