基于光纤光栅与BP神经网络的孔边裂纹监测研究

含孔金属结构的孔边裂纹监测对于保障飞行安全，增强飞机结构可靠性具有重要意义。为实现对孔边裂纹扩展的监测，进行含有孔边角裂纹的含孔铝合金板疲劳加载试验，得到含孔铝合金板试验件的a-N 曲线以及孔边裂纹扩展过程中光纤光栅应变传感器中心波长偏移量；利用包络分析法、BP 神经网络等损伤识别算法对试验数据进行处理与分析；建立能够以光纤光栅应变传感器中心波长偏移量识别孔边裂纹扩展的监测模型，并通过试验对监测模型进行验证。结果表明：此监测模型可有效识别出孔边角裂纹的扩展与穿透，对孔边角裂纹扩展长度监测的准确度达到了97.2%，未来可应用于全机地面疲劳试验、飞机结构健康监测等多种场景。

Research on Monitoring Crack at Hole Edge Based on Fiber Gratings and BP Neural Network

The monitoring of hole edge cracks in metal structures with holes is of great significance for ensuring flight safety and enhancing aircraft reliability. In order to monitor the crack propagation length at the hole edge, the fatigue loading test was carried out on the aluminum alloy plate with hole edge pre-cracks, and the relationship curve between the crack length at the hole edge and fatigue loading times (a-N curve) and the data of the center wavelength of the fiber grating strain sensor changing with the crack propagation at the hole edge were obtained. The method of judging the crack propagation and penetration at the preformed corner by using the center wavelength data was put forward. Then, the envelope of the collected central wavelength data is extracted by extremum method, cubic spline interpolation method and wavelet analysis, and the data envelope is obtained. Finally, the relationship between the data envelope and the a-N curve is analyzed by BP neural network, and a monitoring model that can calculate the crack propagation length at the hole edge through the data collected by the fiber grating strain sensor is obtained, which lays a foundation for the health monitoring of the aircraft structure in the future.