Parameter Identification for Structural Health Monitoring with Extended Kalman Filter Considering Integration and Noise Effect Article Swipe
Liyu Xie
,
Zhenwei Zhou
,
Lei Zhao
,
Chunfeng Wan
,
Hesheng Tang
,
Songtao Xue
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/app8122480
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/app8122480
Since physical parameters are much more sensitive than modal parameters, structural parameter identification with an extended Kalman filter (EKF) has received extensive attention in structural health monitoring for civil engineering structures. In this paper, EKF-based parameter identification technique is studied with numerical and experimental approaches. A four-degree-of-freedom (4-DOF) system is simulated and analyzed as an example. Different integration methods are examined and their influence to the final identification results of the structural stiffness and damping is also studied. Furthermore, the effect of different kinds of noise is studied as well. Identification results show that the convergence speed and estimation accuracy under Gaussian noises are better than those under non-Gaussian noises. Finally, experiments with a five-story steel frame are conducted to verify the damage identification capacity of the EKF. The results show that stiffness with different damage degrees can be identified effectively, which indicates that the EKF is capable of being applied for damage identification and health monitoring for civil engineering structures.
Related Topics
Concepts
Extended Kalman filter
Identification (biology)
Structural health monitoring
Stiffness
Kalman filter
Control theory (sociology)
Invariant extended Kalman filter
Gaussian
Noise (video)
Computer science
System identification
Engineering
Structural engineering
Artificial intelligence
Physics
Data mining
Image (mathematics)
Control (management)
Measure (data warehouse)
Quantum mechanics
Biology
Botany
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app8122480
- https://www.mdpi.com/2076-3417/8/12/2480/pdf?version=1543837416
- OA Status
- gold
- Cited By
- 21
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2903071568
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2903071568Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app8122480Digital Object Identifier
- Title
-
Parameter Identification for Structural Health Monitoring with Extended Kalman Filter Considering Integration and Noise EffectWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-12-03Full publication date if available
- Authors
-
Liyu Xie, Zhenwei Zhou, Lei Zhao, Chunfeng Wan, Hesheng Tang, Songtao XueList of authors in order
- Landing page
-
https://doi.org/10.3390/app8122480Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/8/12/2480/pdf?version=1543837416Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/8/12/2480/pdf?version=1543837416Direct OA link when available
- Concepts
-
Extended Kalman filter, Identification (biology), Structural health monitoring, Stiffness, Kalman filter, Control theory (sociology), Invariant extended Kalman filter, Gaussian, Noise (video), Computer science, System identification, Engineering, Structural engineering, Artificial intelligence, Physics, Data mining, Image (mathematics), Control (management), Measure (data warehouse), Quantum mechanics, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
21Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 7, 2023: 2, 2022: 2, 2021: 3Per-year citation counts (last 5 years)
- References (count)
-
26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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