Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification
Joint Authors
Zhou, Xiaohang
Li, Qiao
Khan, Inamullah
Cao, Lu
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-23
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhou, Xiaohang& Cao, Lu& Khan, Inamullah& Li, Qiao. 2018. Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification. Shock and Vibration،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1215218
Modern Language Association (MLA)
Zhou, Xiaohang…[et al.]. Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification. Shock and Vibration No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1215218
American Medical Association (AMA)
Zhou, Xiaohang& Cao, Lu& Khan, Inamullah& Li, Qiao. Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification. Shock and Vibration. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1215218
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1215218