Substructural Damage Identification by Reduced-Order Substructural Boundaries and Improved Particle Filter With Unknown Input for Non-Gaussian Measurement Noises

Abstract

Substructural identification has shown privileges compared with direct identification of structures. However, unknown substructural interface forces between adjacent substructures are the key but difficult issues in substructural identification. Current substructural identification methods with full-order substructural models still encounter ill-posed identification problems when there are many unknown substructural interaction forces. Thus, it is necessary to study the identification of substructures with reduced-order substructural boundaries. In addition, current substructural identification based on Kalman filtering still assumes that measurement noises are random Gaussian processes. In this paper, a method is proposed for the identification of substructural damage by reduced-order substructural boundaries and an improved particle filter with unknown input for non-Gaussian measurement noises. First, the boundary degrees of freedom of substructural boundaries together with the number of unknown boundary interaction forces are reduced by the characteristic constraint mode approach. Then, based on the reduced-order model of substructure in modal coordinate, an improved particle filter with unknown inputs is proposed, in which the importance density function and particle generation in particle filtering are established by the unscented Kalman filter with unknown inputs. Finally, substructural damage can be identified without the full observations of acceleration responses at the substructure boundaries. The effectiveness of the proposed method is verified through a numerical substructural damage of a planar frame model.