Application of fiber-optic strain sensing technology in high-precision load prediction of aircraft landing gear

In this paper, a compact and lightweight high-precision airborne fiber-optic strain sensor is designed, and a strain-load prediction model based on convolutional neural network and long short-term memory network (ConvLSTM) is proposed to validate the collected landing gear strain data. Firstly, based on the fiber Bragg grating (FBG) sensing principle and simulation validation, small and lightweight strain and temperature sensors were fabricated, and key performance parameters such as sensitivity and linearity were comprehensively investigated, and they were mounted on the left landing gear of the aircraft to conduct strain monitoring by loading a three-way load, and the ConvLSTM model was used to train and test the strain-load mapping with the maximum relative error, average relative error and variance as indicators to evaluate its prediction accuracy and stability. The experimental results show that the strain measurement accuracy is maintained within 2.5 % and the strain sensitivity is as high as 1.3 pm/με within the ± 5000 με measurement range of the strain sensor; the maximum relative errors of the X, Y, and Z load predictions are 6.03 %, 3.75 %, and 4.12 %, respectively, and the overall average relative errors are 2.38 %, 0.27 %, and 0.76 %, with variances of 0.23 N, 0.61 N, and 0.61 N, respectively. 0.23 N, 0.61 N and 0.12 N, indicating that the model predictions are stable and highly accurate, demonstrating higher prediction accuracy when compared with traditional multiple linear regression methods. The results of this research have important application value in the field of aircraft structural health monitoring.