Development of novel acoustic emission source location method utilizing wavelet transform and akaike information criterion

Abstract

The acoustic emission (AE) method enables real-time monitoring of damage initiation and progression. Recently, AE analysis using machine learning has become widely popular; however, the AE source location is often located manually to ensure reliability and accuracy. It is desirable to ensure the AE source location is fully automated with high accuracy when used for machine learning and other applications, or in a situation that generates huge amounts of AE signals. This study proposes a novel AE source location method that can accurately and automatically locate AE sources. First, a wavelet transform was applied to an AE signal to extract the wavelet coefficient of a specific frequency. Then, the Akaike information criterion is applied to the time transient of wavelet coefficient to identify the initial wave arrival time. The localized AE source accuracy of the method is compared with a conventional method. The result of the verification, comparing source location error is nearly same with manual initial detection and the developed method. In addition, about 30 times faster than the conventional visual method. Thus, the developed method is an excellent AE source location method in terms of both accuracy and speed of analysis.