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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 CFRP 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.

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The damage detection of messenger cable is an important guarantee to ensure the safety of electrified railways. The traditional nondestructive testing methods have difficulty in accurately detecting the damage of messenger cable in cladding zone. In this paper, a novel cross-sparse representation (CSR) method based on dispersion dictionary for ultrasonic guided wave (UGW) is proposed for the outer stranded wire of messenger cable in cladding zone. Firstly, the dispersion curves of UGW in multilayer spiral structure are solved by semi-analytical finite element method, to select an appropriate excitation signal. Secondly, the stationary phase approximation method is used to construct dispersion dictionary according to the dispersion curve which can characterize the modes and propagation distance of UGWs, and the CSR is proposed to separate and identify the damage signal. Thirdly, the simulation models of different damages in the outer stranded wire of messenger cable in the cladding zone are established to analyze the correlation between the damage rate and damage index in the cross-sections. Finally, the damage recognition effect based on CSR is analyzed and compared by experiments. The experimental results show that compared with the sparse representation (SR) method using traditional dictionary, the proposed method can improve the average accuracy of damage signal recognition from about 20% to 99.4% within a wide threshold range.

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