2023 Volume 64 Issue 1 Pages 205-211
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. Therefore, it is desirable that the detection of AE source location is fully automated with a high accuracy. This study proposes a novel arrival time identification method for AE source location that can accurately and automatically locate AE sources. First, a wavelet transform is applied to an AE signal to extract the wavelet coefficient of a specific frequency. Subsequently, 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 is compared with conventional arrival time identification methods: visual initial wave arrival time identification, visual S0 mode arrival time identification at the time transient of the wavelet coefficients, automatic peak identification at the time transient of the wavelet coefficients, and automatic AIC wave arrival time identification. The proposed method reduces the number of events with incorrect initial arrival detections. Moreover, the correct detection rate increases by 1.5 times compared to a normal AIC. In addition, the proposed method is approximately 30 times faster than the conventional visual method and is an excellent AE wave arrival time identification in terms of both accuracy and speed of analysis. Lastly, we verify that the proposed method can be effectively applied to anisotropic materials.
