Detection of Anomalous Behavior of a Scaled Laboratory Structure Based on Stochastic Subspace Identification Method

Abstract

Structural health monitoring (SHM) system is an essential part of large structures to allow for early detection of possible damages or defects and avoid human or economic losses. In this paper, stochastic subspace identification (SSI) is employed as a means to obtain the main characteristics of the structure i.e., mode shapes, natural frequencies, etc. Features obtained from SSI are used to assess the health status of the structure. The proposed method is implemented on a scaled three-story laboratory structure where structural damping is altered using a novel magnetic approach. Finite-element analysis is employed to validate the experimental results.