Interlaminar damage assessment method of CFRP laminate based on Synchrosqueezed Wavelet Transform and ensemble Principal Component Analysis

The damage assessment of Carbon Fiber Reinforced Plastics (CFRP) laminate structure is very important to ensure the safety of the structure in service. Machine learning is a research hotspot in damage assessment of CFRP structures. However, there are problems such as noise interference, small number of training samples, and few damage types of training samples. In order to solve the above problems, the damage assessment method of CFRP laminate based on Synchrosqueezed Wavelet Transform and ensemble Principal Component Analysis was proposed. First, the damage feature extraction method based on Synchrosqueezed Wavelet Transform (SWT) and Singular Spectrum Analysis (SSA) was proposed to reduce the feature dimension and achieve effective extraction of damage features. Then, the damage assessment method based on ensemble Principal Component Analysis (PCA) was proposed to realize the damage assessment of CFRP structure. Finally, the simulation experiment was designed to verify the damage assessment results in the case of noise interference, small number of training samples, and few damage types of training samples. The results showed that the damage assessment method of CFRP laminate structure based on SWT, SSA and ensemble PCA was feasible. This paper provided a feasible method for evaluating the damage degree of CFRP laminate structure.

Automated summary

This study proposed a damage assessment method for CFRP laminate structure based on Synchrosqueezed Wavelet Transform and ensemble Principal Component Analysis, which extracted features and conducted damage assessment through PCA, verifying its feasibility under noise interference and limited training samples.