Sensor Distribution Optimization for Composite Impact Monitoring Based on AR Model and LPP

The aim of this article is to provide a sensor distribution optimization method for the effective impact monitoring of composite plates with fewer sensors. In this research, the number of sensors and the minimum difference between categories are used as objective functions I and II, respectively, where the minimum difference is the Euclidean distance between different influence categories. The dual objective functions are defined by means of finite element analysis, the autoregressive (AR) model, and locality−preserving projection (LPP). The sensor distribution is optimized based on Multi−Objective Particle Swarm Optimization (MOPSO). Finally, an impact monitoring method is provided, and an experimental platform is built to verify the method. According to the optimization results, different grid sizes have a certain impact on the identification results, with the smaller the grid size, the smaller the minimum difference between categories. Within a given grid size, the minimum difference between categories increases with the increasing number of sensors. Experiments show that the higher the number of sensors, the higher the recognition rate of the system. Comparing the experimental results with the energy analysis of wavelet bands and PCA methods, it is found that the method used in this study has a higher recognition rate. This research provides an impact monitoring method based on sensor distribution optimization. And the effectiveness of the method is verified by experiments. It provides a useful reference and choice for the structure condition monitoring of composite material plates.