An improved AIC onset-time picking method based on regression convolutional neural network

Akaike information criterion, known as AIC, has become one of the most used methods for acoustic emission (AE) signals onset-time picking since it was proposed in 1970s. However in practical applications, the automatic onset-times picking are hard to perform precisely due to the interference of the strong background noise and static noise, which affects the accuracy of AIC picking. In this work, an improved AIC onset-time picking method based on regression convolutional neural network (CNN) is proposed. First, several features of AE signals to be trained are selected manually, and arrival times of AE signals are labeled correspondingly. Then datasets with features and labels are put into the regression CNN model for training and enhancing the connection of the signals in the time domain. Finally, AIC algorithm is applied to obtain the onset times of the signals processed by the trained CNN model. Based on the Hsu-Nielsen source AE data, the stability and performance of the proposed method are tested, analyzed and compared with those of other mainstream detection methods: AIC, short/long term average combined with AIC (STA/LTA-AIC), and floating threshold (FT). The results prove that the accuracy of the proposed method significantly exceeds that of other methods. Meanwhile, especially in low signal-to-noise ratios (SNRs) scenario, the accuracy stability of the improved method has excellent accuracy and stability, which proves that the proposed method has promising onset-time picking performance for AE signals, including signals with low SNR characteristics.