3-D Source Location by Neural Network for FBG Acoustic Emission Sensors

Abstract

Fiber Bragg grating acoustic emission sensors have been used in many applications. In this paper, based on four fiber Bragg grating acoustic emission sensors, an acoustic emission location experiment is carried out on the surface of a cylindrical polymer-bonded explosive specimen. Due to the difference in the strain sensitivity of fiber Bragg grating in different directions, the traditional time-difference location method is not fit for fiber Bragg grating acoustic emission sensors. A 4-layer dense neural network for fiber Bragg grating acoustic emission sensors, together with a Bayesian regularization method is used to calculate the coordinates of the acoustic emission wave source. The experimental results show that the neural network location method is suitable for fiber Bragg grating acoustic emission sensors.