Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification

Author:

Zhou Xiaohang1ORCID,Cao Lu1ORCID,Khan Inamullah2ORCID,Li Qiao1ORCID

Affiliation:

1. Bridge Engineering Department, Southwest Jiaotong University, Chengdu 610031, China

2. School of Civil Engineering, National University of Sciences and Technology, Risalpur, KPK 24080, Pakistan

Abstract

Data-driven stochastic subspace identification (DATA-SSI) is frequently applied to bridge modal parameter identification because of its high stability and accuracy. However, the existence of abnormal data and noise components may make the identification result of DATA-SSI unreliable. In order to achieve a reliable identification result of the bridge modal parameter, a data inspecting and denoising method based on exploratory data analysis (EDA) and morphological filter (MF) was proposed for DATA-SSI. First, EDA was adopted to inspect the data quality for removing the data measured from malfunctioning sensors. Then, MF along with an automated structural element (SE) size determination technique was adopted to suppress the noise components. At last, DATA-SSI and stabilization diagram were applied to identify and exhibit the bridge modal parameter. A model bridge and a real bridge were used to verify the effectiveness of the proposed method. The comparison of the identification results of the original data and improved data was made. The results show that the identification results obtained with the improved data are more accurate, stable, and reliable.

Funder

National Basic Research Program of China

Publisher

Hindawi Limited

Subject

Mechanical Engineering,Mechanics of Materials,Geotechnical Engineering and Engineering Geology,Condensed Matter Physics,Civil and Structural Engineering

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