Fiber bragg grating sensing dynamic load identification method based on AR model and mahalanobis distance

Abstract

The invention discloses a fiber bragg grating sensing dynamic load identification method based on AR model and mahalanobis distance. The method comprises the following steps: arranging the position of a distributed fiber bragg grating sensing network; monitoring and collecting impact response dynamic signals in real time; analyzing time domains of impact response signals and determining a response spectrum characteristic frequency capable of representing impact position information; extracting wavelet-packet-analysis-based spectrum response characteristic frequency and building an AR model parameter matrix; judging similarity between the impact position signals and response signals in a sample library of the AR model parameter matrix by using mahalanobis distance, primarily determining the area of the impact load to be monitored according to three impact positions with high similarity, then accurately identifying the impact load position by using a triangular center location method. Since a fiber bragg grating demodulating system is relatively low in demodulating frequency, the impact load cannot be located by using a time difference method; compared with the conventional time domain locating method, the method is simple and reliable.

Description

technical field [0001] The invention discloses a fiber grating sensing dynamic load identification method based on an AR model and a Mahalanobis distance, and relates to the technical field of health monitoring of intelligent materials and structures. Background technique [0002] Shock load identification began in the 1970s. With the rapid development of the aviation industry, people have increased the requirements for aircraft flight performance. Therefore, a large number of new materials have been applied to the aircraft. In order to ensure the safety and reliability of the aircraft in flight, the identification of impact loads came into being. The identification of impact load mainly includes frequency domain identification method, time domain identification method, artificial neural network identification method and other methods. [0003] In 1985, Han Erzhong and others applied the experimental modal analysis method to study the load effect of vibrating objects, and ...

AI Technical Summary

Problems solved by technology

The neural network positioning method based on prior knowledge generally needs to build a suitable neural network model, and obtain a large number of sample points and test points for training and testing of the neural network, so it cannot be used in the engineering field on a large scale

Images