Open AccessData Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification
Author(s) -
Xiaohang Zhou,
Lu Cao,
Inamullah Khan,
Li Qiao
Publication year - 2018
Publication title -
shock and vibration
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 45
eISSN - 1875-9203
pISSN - 1070-9622
DOI - 10.1155/2018/3926817
Subject(s) - identification (biology) , subspace topology , bridge (graph theory) , modal , noise (video) , noise reduction , computer science , data mining , data quality , data reduction , filter (signal processing) , stability (learning theory) , pattern recognition (psychology) , artificial intelligence , engineering , machine learning , computer vision , medicine , metric (unit) , chemistry , botany , operations management , polymer chemistry , image (mathematics) , biology
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.