The Research of Acoustic Emission Signal Classification

Author

Meng, Xiaojing ; Liu, Weidong ; Ding, Enjie

Author_Institution

Sch. of Xuhai, China Univ. of Min. & Technol., Xuzhou, China

fYear

2011

fDate

14-16 Oct. 2011

Firstpage

41

Lastpage

44

Abstract

When using acoustic emission (AE) monitoring of rock burst, the signals received by AE monitoring device are related to the type of AE source type, which influences predicting the AE monitoring accuracy. In view of the time-varying characteristics of acoustic emission signals, we adopt the Short-Time analysis technology, in other words, to acoustic emission signal transient analysis technique to extract the signal characteristics of effective, then the fisher criteria for the number of signal compression. Using neural network technology for signal classification, the classification results showed that this method is particularly effective in terms of the Acoustic Emission signal.

Keywords

acoustic signal processing; data compression; neural nets; signal classification; transient analysis; AE signal monitoring accuracy; AE signal monitoring device; AE source type; acoustic emission signal classification; acoustic emission signal monitoring device; acoustic emission signal transient analysis technique; neural network technology; short-time analysis technology; signal characteristic extraction; signal compression; time-varying characteristics; Acoustic emission; Educational institutions; Monitoring; Rocks; Signal processing algorithms; Time frequency analysis; Acoustic Emission; Fisher Criterion; Neural Network; Short time analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2011 Seventh International Conference on

Conference_Location

Dalian

Print_ISBN

978-1-4577-1397-2

Type

conf

DOI

10.1109/IIHMSP.2011.101

Filename

6079529