基于FBG和BP神经网络的声发射定位方法

针对传统基于神经网络的声发射定位选择的样本不具有广泛代表性的问题,提出了基于光纤布拉格光栅(fiber Bragg grating,简称FBG)传感和反向传播(back propagation,简称BP)神经网络的声发射定位技术。该方法使用提取的窄带信号间时差作为样本,无需考虑损伤特性及波速对定位的影响,同时使用Levenberg-Marquardt(简称LM)算法训练样本,提高了BP神经网络的训练速度。在500mm×500mm的铝合金板上进行实验,声发射定位的平均误差为6.65mm,验证了该方法的有效性和准确性。

Acoustic Emission Location Method Research Based on FBG Network and BP Neural Network

Aiming at the samples selected by the traditional acoustic emission location based on neural network is not widely representative, acoustic emission localization technologies based on fiber Bragg grating (FBG) network and BP neural network are proposed. Time difference between the extracted narrowband signals is used as training sample. According to the back propagation (BP) neural network, acoustic emission sources are located without a large number of samples of difference characteristics acoustic emission and wave velocity. To solve the problem of slow convergent rate in gradient-descent algorithm, Levenberg-Marquardt algorithm is applied to improve convergent rate. The algorithm is verified in 500 mm×500 mm aluminum alloy plate. The average localization accuracy of aluminum alloy plate is 6.65 mm, which verifies the validity and accuracy of the method.