Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: IResearch Article A Machine Learning Approach for Locating Acoustic Emission | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 895486 14 pages doi 2010 895486 Research Article A Machine Learning Approach for Locating Acoustic Emission N. F. Ince 1 Chu-Shu Kao 2 M. Kaveh 1 A. Tewfik EURASIP Member 1 and J. F. Labuz2 1 Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN 55455 USA 2Department of Civil Engineering University of Minnesota Minneapolis MN 55455 USA Correspondence should be addressed to N. F. Ince ince_firat@ Received 18 January 2010 Revised 26 July 2010 Accepted 20 October 2010 Academic Editor Joao Marcos A. Rebello Copyright 2010 N. F. Ince et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. This paper reports on the feasibility of locating microcracks using multiple-sensor measurements of the acoustic emissions AEs generated by crack inception and propagation. Microcrack localization has obvious application in non-destructive structural health monitoring. Experimental data was obtained by inducing the cracks in rock specimens during a surface instability test which simulates failure near a free surface such as a tunnel wall. Results are presented on the pair-wise event correlation of the AE waveforms and these characteristics are used for hierarchical clustering of AEs. By averaging the AE events within each cluster super AEs with higher signal to noise ratio SNR are obtained and used in the second step of the analysis for calculating the time of arrival information for localization. Several feature extraction methods including wavelet packets autoregressive AR parameters and discrete Fourier transform coefficients were employed and compared to identify crucial patterns related to P-waves in time and frequency domains. By using the extracted features an SVM .
