Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification
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 ach...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/3926817 |
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| _version_ | 1832548418300411904 |
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| author | Xiaohang Zhou Lu Cao Inamullah Khan Qiao Li |
| author_facet | Xiaohang Zhou Lu Cao Inamullah Khan Qiao Li |
| author_sort | Xiaohang Zhou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aa05c01b72a74fe8b75ff118c1c4c8da |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-aa05c01b72a74fe8b75ff118c1c4c8da2025-02-03T06:14:06ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/39268173926817Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace IdentificationXiaohang Zhou0Lu Cao1Inamullah Khan2Qiao Li3Bridge Engineering Department, Southwest Jiaotong University, Chengdu 610031, ChinaBridge Engineering Department, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Civil Engineering, National University of Sciences and Technology, Risalpur, KPK 24080, PakistanBridge Engineering Department, Southwest Jiaotong University, Chengdu 610031, ChinaData-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.http://dx.doi.org/10.1155/2018/3926817 |
| spellingShingle | Xiaohang Zhou Lu Cao Inamullah Khan Qiao Li Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification Shock and Vibration |
| title | Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification |
| title_full | Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification |
| title_fullStr | Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification |
| title_full_unstemmed | Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification |
| title_short | Data Inspecting and Denoising Method for Data-Driven Stochastic Subspace Identification |
| title_sort | data inspecting and denoising method for data driven stochastic subspace identification |
| url | http://dx.doi.org/10.1155/2018/3926817 |
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