Health Monitoring Of Three-dimensional Six-directional Braided Composites Based On FBG And PCA

Composite materials have been widely used in aerospace,automotive,high-speed railway and building materials because of their excellent specific stiffness,specific strength,good durability and fatigue resistance.Real time and efficient nondestructive testing methods are great significance for the internal damaged examination and on-line monitoring of composite structures.In this thesis,the FBG sensor and the singular value decomposition algorithm are used in the combination for the damaged examination of three-dimensional(3D)braided composite material preform.Based on this method,we performed the experimental of the damage occurrence identification and location detection of resin composites and constructed the measurement system damage inspection of 3D braided composite materials based on FBG.In this thesis,firstly,the basic definition of composite material is introduced,and the importance of nondestructive examination of composite material based on wide application in various fields such as aviation is analyzed,and the composite health monitoring research status at home and abroad is described.Secondly,the sensing principle and preparation method of FBG are described and the sensing characteristics of FBG are analyzed,and the theoretical basis for the damage detection of 3D braided composites with FBG sensor is given.Subsequently,we introduces the braiding method of FBG sensor embedded into 3D braided composite material preform,in which FBG sensor as axial and sixth-directional yarn is embedded into composite materials preform using 3D four-step six-directional method.According to the yarn carriers motion law in four-step braiding process of 3D six-directional braiding composite material,we build the three unit cell geometric model of structure,research the in-plane and spatial trajectory in different regions,and predict the direction of FBG sensor in composite material.Based on the 3D six-directional braided composites,we propose a method of the intelligent composite material construction by using FBG sensors embedded into 3D braided composites,in which the characteristics of FBG sensor are studied.In this thesis,the fundamental principle of the principal component analysis(PCA),data acquisition and analysis,matrix conversion,and a series of related contents of damage detection are introduced.The relevant statistics of PCA monitoring and its availability are analyzed.The structural health monitoring process using PCA is described and the flow chart is drawn.Based on PCA method,a detection algorithm of structure damage is put forward,by which the method to construct the structure health monitoring system of 3D six-directional braided composites is proposed.The experimental results show that the FBG sensor can accurately measure the internal damage of composite materials,and the PCA method can be used to accurately calculate the internal defects of five-dimensional braided composites.This study provides a theoretical basis for health monitoring of 3D braided composites in aerospace applications.Using PCA,the appropriate number of principal components is selected,and then the reference database of tensile-recovery experiments with undamaged preforms is built.Subsequently,the preform is damaged by loading to the fracture strength,then the Q statistics and the T~2 statistics are determined according to the output values of the FBG sensor,of which the results are compared with the baseline data to form conclusions of the damage identification and localization.The specific information of damaged preform can be analyzed using damage index value of experiments,and the damage index of the damaged preform is much larger than that of the undamaged preform.The variation regulation of the Q and the T~2 value of preform during the tensile process are basically the same,and the Q value is far less than the T~2 value.The T~2value can well reflect the large damage of the preform,and the Q value can reflect the details of the damage.