Improved Stochastic Subspace System Identification for Structural Health Monitoring

11th International Conference on Damage Assessment of Structures

物理学;材料科学

Chang, Chia-Ming^1 ; Loh, Chin-Hsiung^1

Department of Civil Engineering, National Taiwan University, Taipei

10617, Taiwan^1

关键词: Computational expense;    Dynamic characteristics;    Real-time application;    Sensor measurements;    Singular spectrum analysis;    Stochastic subspace;    Structural information;    System identification methods;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/628/1/012010/pdf
DOI  :  10.1088/1742-6596/628/1/012010

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Structural health monitoring acquires structural information through numerous sensor measurements. Vibrational measurement data render the dynamic characteristics of structures to be extracted, in particular of the modal properties such as natural frequencies, damping, and mode shapes. The stochastic subspace system identification has been recognized as a power tool which can present a structure in the modal coordinates. To obtain qualitative identified data, this tool needs to spend computational expense on a large set of measurements. In study, a stochastic system identification framework is proposed to improve the efficiency and quality of the conventional stochastic subspace system identification. This framework includes 1) measured signal processing, 2) efficient space projection, 3) system order selection, and 4) modal property derivation. The measured signal processing employs the singular spectrum analysis algorithm to lower the noise components as well as to present a data set in a reduced dimension. The subspace is subsequently derived from the data set presented in a delayed coordinate. With the proposed order selection criteria, the number of structural modes is determined, resulting in the modal properties. This system identification framework is applied to a real-world bridge for exploring the feasibility in real-time applications. The results show that this improved system identification method significantly decreases computational time, while qualitative modal parameters are still attained.