Truss Structure Health Monitoring Based on Electromechanical Impedance Method

Abstract

Truss structure is widely used in civil engineering applications for its advantages of easy transportation, convenient assembly and uniform loading. However, it is difficult to achieve real-time health monitoring because of connection diversity and complexity of truss structures. As a novel structural health monitoring technique, electro-mechanical impedance method could monitor the health state of one structure by measuring the spectra of impedance or admittance of the piezoelectric elements, which are bonded on the surface of this structure. This approach has the advantages of nonparametric model analysis, easy sensor installation and high local sensitivity, especially in sensitive frequency range. The damage information, which is tested and recorded by using electromechanical impedance method, could convert into intuitive results through neural network because of its good ability for nonlinear mapping. In this paper, a three-layer assembly truss structure was chosen as experimental object, piezoelectric elements were bonded on structure joints to measure structural impedance spectra, the change of these structural impedance spectra was tested and recorded under high frequency excitations when different truss bars were loosed, and then, one back-propagation (BP) neural network was built and trained by this damage information, which were treated as input samples. These results show that the sensitivity of impedance method is not the same to different frequency range and trained neural network could quickly identify loosen truss bars.