Application of response reconstruction method of wing box structure based on neural network

Abstract

It is of great practical significance for real-time health monitoring to reconstruct other position responses by using limited measuring point information of wing box structure in complex navigation with harsh bearing conditions. The nonlinear relationship between the responses is obtained by training the back propagation neural network, and the response reconstruction method based on neural network is established and verified by numerical simulation through finite element analysis. The method is applied to the response reconstruction, damage location and judgment analysis of typical load-bearing structures of wing boxes under measured random excitation environment.The results show that the root mean square relative error of the predicted response power spectral density reconstructed by this method is less than 1.90 dB and the main frequency error is less than 10%. The damage or fault of the key measuring point e of the wing box occurred 3 s after the intercepted fragment data, and its fault characteristic frequency is about 240 Hz. The method is feasible to response reconstruction prediction and health monitoring analysis.

Keywords

• neural network
• wing box
• random vibration
• response reconstruction
• health monitoring