Damage assessments of composite under the environment with strong noise based on synchrosqueezing wavelet transform and stack autoencoder algorithm

This study proposes a technique for damage location and quantitative identification for composites under strong noise background on the basis of synchro squeezing wavelet transform and stack autoencoder algorithm. Firstly, the simulation of the mechanism of Lamb wave and damage of different degrees were conduct in this stage. Secondly, the simulation of the actual damage was carried out in the selecting composites with strain field of the structure changed by different masses (within the range of 3 grades) and mass blocks at different locations (64 regions). Moreover, the white noise (3 dB) was introduced to the signal detected by the sensor in order to simulate the Lamb Wave signals collected from the sensors under the background of strong noise. Actually, the training sample is composed of 26,880 (3 x 64 x 140) signals. In addition, the synchronized wavelet transform was proposed to denoise the signal through employing strong noise. After that, Fourier Transform was applied to extract damaged frequencies. Additionally, the input of the stack autoencoder was the frequencies, and that output was the result of corresponding damage, and in the meanwhile the damage identification model was established as well. Finally, the test data which had been processed was then input into the damage identification model for further testing and analysis. It was finally concluded that 191(total 192) samples were correctly identified, and the correct recognition rate arrived as high as 99.48%. It can identify different degrees of damage in different locations. It was demonstrated by the results that the method is highly accurate, with extensive application potential in instantaneous locating and quantitative recogniting damages in the composite plate under the condition of strong noise exists. (C) 2020 Elsevier Ltd. All rights reserved.