Research On Interface Damage Identification Of Laminated Plates By Electro-mechanical Impedance Method

The laminated composite structures are usually damaged due to the interfacial drawbacks.The interfacial damages of the large and complex structures can be caused by the natural disasters,external environment and long-term loadings etc.,which are hard to detect and will make the mechanical properties of the structure seriously degraded and even cause the overall destruction.In order to make full use of the performance and improve its value of the laminated structure,it is necessary to study the inter-interface mechanical behavior.At the same time,it is also necessary to find an effective measure for real-time health monitoring to the laminated structures.Electro-mechanical impedance method is a smart materialsbased structural health monitoring technology,and is usually used in the field of civil engineering due to its sensitivity to minor structural damages.The structural damages can be indentified from the extracted EMI signatures based on EMI model.However,due to the large EMI signal data in the actual project,how to deal with these data quickly and effectively has always been the difficult point of the study.Compression sensing theory combined with BP neural network provides a feasible way for large data processing.This method is able to compress the signal without distortion and can greatly reduce the dimension of the signal.In this paper,we first focus on the imperfect interface problem of laminated plates.The viscoelastic interface law is introduced in the mechanical analysis and then the analytical solution of elasticity of the composite laminates can be obtained by the state space method.Meanwhile,the electro-mechanical effect of the laminated plate containing the piezoelectric layer is considered and the conductance relations can be derived.Furthermore,we establish the isotropic and anisotropiclaminate FEM model to simulate the different interfacial damage degree by setting different stiffness coefficients to the spring elements of the interfacial layers and then the corresponding EMI signals can be extracted.Compressing the signal by compressed perception theory improves the transmission and storage capacity of the signal.We employ the principal component analysis to reduce the dimension of input principal component eigenvalues ? to BP neural network.The results show that the EMI technique can detect the interface damage of the laminated structure effectively.The combination of compression perception and BP neural network can reduce the dimension of the signature data,achieve the fast transmission and storage of the signals and quantify the degree of interfacial damages.Thus,the proposed method provides a feasible way for structural health monitoring.