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Abstract Skin is an important structural component which covers the surface of aircraft widely, the structural health of skin has a great impact on the overall safety of aircraft. Due to the harsh operating environment, complex and changing working conditions, and high-intensity service process of aircraft, it is easy to cause damage to the skin. Thus, the in-situ health monitoring for aircraft skin is an effective approach to ensure its safety and reliability. Thanks to the ability of Lamb waves to propagate over long distances and recognize small-scale defects, they have shown great potential in the health monitoring of thin-walled structures and have received widespread attention. However, the traditional Lamb wave-based damage identification methods generally suffer from the problems of large number of sensors and low efficiency. Therefore, a skin damage identification method based on power spectral entropy is proposed in this paper, which can estimate the damage extent by extracting the nonlinear component increase caused by damage, and the experiments validate that the proposed method has higher sensitivity and computational efficiency than the existing other entropy-based methods.
Zhou et al. (Wed,) studied this question.