Real-time damage analysis of 2D C/SiC composite based on spectral characters of acoustic emission signals using pattern recognition

In this study, unsupervised and supervised pattern recognition were implemented in combination to achieve real-time health monitoring. Unsupervised recognition (k-means++) was used to label the spectral characteristics of acoustic emission (AE) signals after completing the tensile tests at ambient temperature. Using in-plane tensile at 800 and 1000 degrees C as implementing examples, supervised recognition (K-nearest neighbor (KNN)) was used to identify damage mode in real time. According to the damage identification results, four main tensile damage modes of 2D C/SiC composites were identified: matrix cracking (122.6-201 kHz), interfacial debonding (201-294.4 kHz), interfacial sliding (20.6-122.6 kHz) and fiber breaking (294.4-1000 kHz). Additionally, the damage evolution mechanisms for the 2D C/SiC composites were analyzed based on the characteristics of AE energy accumulation curve during the in-plane tensile loading at ambient and elevated temperature with oxidation. Meanwhile, the energy of various damage modes was accurately calculated by harmonic wavelet packet and the damage degree of modes could be analyzed. The identification results show that compared with previous studies, using the AE analysis method, the method has higher sensitivity and accuracy.

Automated summary

Unsupervised and supervised pattern recognition methods were implemented in combination to achieve real-time health monitoring in this study. Spectral characteristics of acoustic emission (AE) signals were labeled using unsupervised recognition, while supervised recognition was used to identify damage mode in real time. The study identified four main tensile damage modes and analyzed the damage evolution mechanisms for 2D C/SiC composites.