A machine learning approach for locating acoustic emission

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 classifier fused with probabilistic output is used to recognize the P-wave arrivals in the presence of noise. Results show that the approach has the capability of identifying the location of AE in noisy environments.