An Experimental Study of Structural Health Monitoring Using Incomplete Measurements

Abstract

In this paper, algorithmic approaches to enhance structural health monitoring capability when faced with incomplete measurements are addressed. The incomplete measurement problem has two aspects: (i) experimental measurement of a lesser number of modes of vibration than that of the analytical model and (ii) experimental measurement of a lesser number of degrees of freedom than that of the analytical model. Studies comparing model reduction, eigenvector expansion, and a hybrid model reduction/eigenvector expansion to address the second contribution are performed using experimental data. These approaches to the incomplete measurement problem are evaluated within the frameworks of multiple-constraint matrix adjustment (both sparsity and nonsparsity preserving algorithms) and minimum rank perturbation theory, which are both applicable for model refinement as well as damage location. Experimental evaluation of the proposed approaches utilize data from the NASA Langley Research Center 8-bay truss and McDonnell Douglas Aerospace 10-bay truss facilities.