An intelligent ultrasonic guided wave deep imaging detection method considering the propagation characteristics of each direction

Abstract Macroscopic damage in multilayer anisotropic structures is usually formed by the rapid development of material micro-damage. The existing micro-damage imaging detection technology does not consider the difference of wave velocity in all directions of the detection structure, especially not directly taking the wave velocities in different direction into the imaging counting process, the micro-damage imaging detection, and the false detection rate and missed detection rate are high. In this paper, a deep learning imaging detection method considering velocity in all directions is proposed and verified on a carbon fiber anticorrosive coating structure of a shaftless ring propeller drive system. Firstly, the problem that the elliptical damage path cannot be determined in the anisotropic structure is analyzed, and the omnidirectional velocity of the carbon fibre reinforced plastics structure is obtained through simulation analysis. A new omni-directional imaging method was proposed, which discretized the monitored objects and acquired the damage index through deep learning network. The damage propagation time of the reference point was compared with that of the actual damage point to determine the damage probability of the structure. The experimental results show that the omni-directional imaging method can accurately and intuitively display the damage information of anisotropic structures.