Application of Hilbert–Huang Transform to acoustic emission signal for burn feature extraction in surface grinding process

Measurement ◽  
2014 ◽  
Vol 47 ◽  
pp. 14-21 ◽  
Author(s):  
Zhensheng Yang ◽  
Zhonghua Yu ◽  
Chao Xie ◽  
Youfang Huang
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Acoustic Emission Signal ◽  
Surface Grinding ◽  
Grinding Process ◽  
Hilbert Huang Transform ◽  
Emission Signal

Experiment on Surface Roughness and Acoustic Emission Signal in Infeed Period of Cylindrical Plunge Grinding

2013 ◽  
Vol 690-693 ◽  
pp. 2442-2445 ◽  
Author(s):  
Hao Lin Li ◽  
Hao Yang Cao ◽  
Chen Jiang
Keyword(s):  
Surface Roughness ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Feed Rate ◽  
Grinding Process ◽  
Experiment Research ◽  
Plunge Grinding ◽  
Emission Signal ◽  
Ae Signal ◽  
Ae Signals

This work presents an experiment research on Acoustic emission (AE) signal and the surface roughness of cylindrical plunge grinding with the different infeed time. The changed infeed time of grinding process is researched as an important parameter to compare AE signals and surface roughnesses with the different infeed time in the grinding process. The experiment results show the AE signal is increased by the increased feed rate. In the infeed period of the grinding process, the surface roughness is increased at first, and then is decreased.

Feature extraction of acoustic emission signal for diamond scratching of optical glass BK7

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
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Acoustic Emission Signal ◽  
Optical Glass ◽  
Emission Signal

Research on Acoustic Emission Test Method for Metal Fatigue Fracture Based on Wavelet Packets Feature Extraction

2011 ◽  
Vol 488-489 ◽  
pp. 432-435
Author(s):  
Qi Wang ◽  
Yin Sheng Chen ◽  
Kai Song
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Fatigue Crack ◽  
Crack Propagation ◽  
Fatigue Fracture ◽  
Fatigue Crack Propagation ◽  
Acoustic Emission Signal ◽  
Wavelet Packets ◽  
Metal Fatigue ◽  
Emission Signal

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.

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