Fuzzy identification of cutting acoustic emission with extended subtractive cluster analysis

2011 ◽  
Vol 67 (4) ◽  
pp. 2599-2608 ◽  
Author(s):  
Qun Ren ◽  
Luc Baron ◽  
Marek Balazinski
Author(s):  
Zhongzheng Zhang ◽  
Cheng Ye ◽  
Jun Jiang

In order to study acoustic emission (AE) signals characteristics of pitting corrosion on carbon steel, Pitting corrosion process on carbon steel in 6% ferric chloride solution was monitored by AE technology. K-mean cluster algorithm was used to classify the monitored AE signals, in which the duration, counts, amplitude, absolute energy and peak frequency were analyzed as the AE signals characteristics, and different types AE sources were identified. The results showed that there were mainly three type AE sources during carbon steel pitting corrosion process in ferric chloride solution, and the different types AE sources could be classified by cluster analysis. The research results have some certain significance for AE monitoring of pitting corrosion on carbon steel.


2015 ◽  
Vol 50 (14) ◽  
pp. 1921-1935 ◽  
Author(s):  
Li Li ◽  
Yentl Swolfs ◽  
Ilya Straumit ◽  
Xiong Yan ◽  
Stepan V Lomov

2018 ◽  
Vol 96 (11) ◽  
pp. 969-987
Author(s):  
J. Destouesse ◽  
M. Diakhate ◽  
C. Badulescu ◽  
D. Thévenet ◽  
M. Stackler ◽  
...  

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