Research on hole edge crack monitoring based on optical fiber gratings and BP neural network

Abstract

The hole edge crack monitoring of metal structures with holes is of great significance for ensuring flight safety and enhancing the reliability of aircraft structures. In order to monitor the crack growth at the hole edge, the fatigue loading test of porous aluminum alloy plate containing the corner crack at the hole edge is carried out, and the a-N curve of the test piece of porous aluminum alloy plate and the center wavelength offset of the optical fiber grating strain sensor during the crack growth at the hole edge are obtained. The damage identification algorithms such as envelope analysis method and BP neural network are used to process and analyze the test data. The monitoring model that can identify the crack growth at the hole edge with the center wavelength offset of the optical fiber grating strain sensor is established, and verified with test parts. The results show that the established monitoring model can effectively identify the propagation and penetration of the corner crack at the hole edge, and the accuracy of monitoring the propagation length of the corner crack at the hole edge has reached 97.2%, which can be applied to the ground fatigue test of the whole aircraft, aircraft structure health monitoring and other scenarios in the future.

Keywords

• hole edge crack
• optical fiber grating
• envelope analysis
• bp neural network
• monitoring model