基于AR模型的声发射信号到达时间自动识别

声发射信号到达时间的信息,对于声发射事件的定位、识别以及声发射源机理分析都是非常重要的。实际应用中，常用人工读取或通过设定幅值阈值来获取信号的到达时间。针对以上常用方法的缺点，本文结合噪声信号的AR模型和声发射信号的AR模型，应用Akaike信息准则，实现了对声发射信号到达时间的自动识别。对实验数据的识别结果显示，该方法对信号的幅频特性变化比较敏感。在相同信噪比的情况下，该方法识别的偏差要小于阈值法。当信噪比较低时，阈值法可能会给出错误的结果，而该方法仍然能够给出较准确的结果。

Automatic identification of arrival time of acoustic emission signal by means of AR model

The information of acoustic emission (AE) signal's arrival time is very important for event location, event identification and source mechanism analysis. In practical, engineering, the manual picking and amplitude threshold setting up are routinely used to determine the arrival time of acoustic emission signal. To improve the defect of commonly used method, the first arrival time of AE signal was automatically determined through combining AR model of the noise with the acoustic emission signal based on Akaike's information criterion (AIC). The results show that the proposed method is more sensitive to the change of amplitude-frequency characteristics in contrast to the amplitude threshold method. In case the signal-to-noise ratio (SNR) takes the same value, the error of the approach is less than that of threshold detection. Especially, when the SNR is so law that the detection by use of threshold will be no longer effective, the results of the approach are still of enough accuracy.