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Multi-scale entropy based damage detection for thermal protection structures with variation of ambient temperature

The thermal protection structures of supersonic vehicles are vulnerable to damage in the extreme environment of high temperature. With advantages of the large propagation range and high sensitivity to damage, guided waves show great potential for structural health monitoring. However, guided waves are susceptible to ambient temperature change, resulting in low reliability of damage detection results. Therefore, this paper proposed a multi-scale entropy feature extraction method for structural damage detection. Firstly, to eliminate the influence of multi-mode guided waves, a sliding window was used to extract the wave packet of different signals to analyze disturbance caused by damage and temperature. Then, an ant colony optimization algorithm was introduced as a feature fusion method to improve the classification performance. To evaluate the performance of features selected by the ant colony optimization algorithm, K-means clustering algorithm, and silhouette coefficients were utilized to calculate the evaluation function and represent the characteristics of damage. Finally, a set of guided wave signals from 20°C to 40°C were obtained to investigate the influence of temperature variation on the damage feature extraction. The results show that the multi-scale entropy method based on the sliding window can effectively extract damage features in the condition of temperature change.