Blind source separation-based optimum sensor placement strategy for structures

Abstract

Optimal sensor placement (OSP) plays a key role towards cost-effective structural health monitoring (SHM) of flexible structures like tall buildings. In the context of SHM, vibration measurements collected from a large array of sensors involve significant data storage, signal processing, and labor-intensive deployment of sensors. Moreover, with the increasing cost of vibration sensors, it is not possible to install sensors at all locations of the structure. To circumvent these practical challenges, the OSP provides a powerful mathematical framework to estimate unknown structural information based on optimally instrumented sensors located at fewer locations. The proposed research is focused on determination of optimum sensor positions in the multi-degrees-of-freedom system using blind source separation (BSS) method. The underdetermined signal separation capability of the BSS is explored to conduct the modal identification using fewer sensors and the resulting modal parameters are utilized to set up the optimization criterion for optimal sensor configurations. The methodology is illustrated using simulation models with different mass and stiffness distributions under a wide range of ground motion characteristics. Finally, the vibration measurements of the Canton tower data in China are utilized to demonstrate the performance of the proposed OSP technique.