Title :
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
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;
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
DOI :
10.1109/IIHMSP.2011.101
