Abstract
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.
Highlights
Changing environmental conditions and harsh mechanical loading are sources of damage to structures
We describe a novel combination of signal processing and machine learning techniques based on hierarchical clustering and support vector machines to process multi-sensor acoustic emissions (AEs) data generated by the inception and propagation of microcracks in rock specimens during a surface instability test
Novel approaches based on hierarchical clustering and support vector machines (SVM) are introduced for clustering AE signals and detecting primary wave (P-wave) for microcrack location in the presence of noise
Summary
Changing environmental conditions and harsh mechanical loading are sources of damage to structures. An AE system that automatically “learns” crucial patterns from the total AE data, as well as particular P-wave arrivals, may provide clues for distinguishing between real events and extraneous signals, improving the spatial accuracy of AE locations and reduce false alarms. Accurate detection of these events with appropriate signal processing and machine learning techniques may open new possibilities for monitoring the health of critical components; this offers the possibility for raising alarms in an automated manner if the degradation of structural integrity is severe. The experimental results on the spatial distributions of AE events are provided and compared to the actual fracture locations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
