Structural Parameters Identification Using PZT Sensors and Genetic Algorithms
Piezoelectric ceramic lead zirconate titanate (PZT) based electro-mechanical impedance (EMI) technique for structural health monitoring (SHM) has been successfully applied to various engineering systems [1-5]. In the traditional EMI method, statistical analysis methods such as root mean square deviation indices of the PZT electromechanical (EM) admittance are used as damage indicator, which is difficult to specify the effect of damage on structural properties. This paper proposes to use the genetic algorithms (GAs) to identify the structural parameters according to the changes in the PZT admittance signature. The basic principle is that structural damage, especially local damage, is typically related to changes in the structural physical parameters. Therefore, to recognize the changes of structural parameters is an effective way to assess the structural damage. Towards this goal, a model of driven point PZT EM admittance is established. In this model, the dynamic behavior of the structure is represented by a multiple degree of freedom (DOF) system. The EM admittance is formulated as a function of excitation frequency and the unknown structural parameters, i.e., the mass, stiffness and the damping coefficient of many single DOF elements. Using the GAs, the optimal values of structural parameters in the model can be back-calculated such that the EM admittance matches the target value. In practice, the target admittance is measured from experiments. In this paper, we use the calculated one as the target. For damage assessment, these optimal values obtained before and after the appearance of structural damage can be compared to study the effects of damage on the structural properties, which are specified to be stiffness and damping in this study. Furthermore, the identified structural parameters could be used to predict the remaining loading capacity of the structure, which serves the purpose for damage prognosis.
Yansheng Yin and Xin Wang
Y. W. Yang and A. W. Miao, "Structural Parameters Identification Using PZT Sensors and Genetic Algorithms", Advanced Materials Research, Vols. 79-82, pp. 63-66, 2009