elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

System identification and tracking using a statistical model and a Kalman filter

Soal, Keith Ian and Govers, Yves and Bienert, Jörg and Bekker, Anriëtte (2019) System identification and tracking using a statistical model and a Kalman filter. Mechanical Systems and Signal Processing, 133 (106127). Elsevier. doi: 10.1016/j.ymssp.2019.05.011. ISSN 0888-3270.

Full text not available from this repository.

Official URL: https://doi.org/10.1016/j.ymssp.2019.05.011

Abstract

The sensitivity of system identification estimates to changing environmental and operational parameters is important for structural health monitoring and inverse force estimation. Damage to a structure can be misidentified or masked by modal shifts as a result of environmental parameters. In this paper a novel approach to reduce the uncertainties and improve the sensitivity of system identification and tracking is presented based on a data driven statistical model and a Kalman filter. A key objective is to make experimental data maximally informative by using additional system inputs. The method is first demonstrated on numerical data where it was found to improve accuracy and identify underlying trends. Investigations were then conducted on full scale data from the research vessel Polarstern. Model training led to the development of a sliding predictive model using an optimized linear regression method. The model was found to accurately re-create the training data set and was used to make predictions based on future system inputs. Since both the model prediction and the system identification estimates contain different uncertainties the Kalman filter was used to combine both estimates in an optimal way. The Kalman filter estimates were observed to produce balanced and consistent results. The Kalman estimates were also not overly or consistently biased by the SSI estimates or the model predictions.

Item URL in elib:https://elib.dlr.de/130041/
Document Type:Article
Title:System identification and tracking using a statistical model and a Kalman filter
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Soal, Keith IanKeith.Soal (at) dlr.dehttps://orcid.org/0000-0002-5132-6823
Govers, YvesYves.Govers (at) dlr.dehttps://orcid.org/0000-0003-2236-596X
Bienert, JörgJoerg.Bienert (at) thi.deUNSPECIFIED
Bekker, Anriëtteannieb (at) sun.ac.zaUNSPECIFIED
Date:9 May 2019
Journal or Publication Title:Mechanical Systems and Signal Processing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:133
DOI :10.1016/j.ymssp.2019.05.011
Publisher:Elsevier
ISSN:0888-3270
Status:Published
Keywords:system identification, operational modal analysis, ship structures, multivariate statistics, Kalman filter, automated modal parameter selection
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:fixed-wing aircraft
DLR - Research area:Aeronautics
DLR - Program:L AR - Aircraft Research
DLR - Research theme (Project):L - Flight Physics (old)
Location: Göttingen
Institutes and Institutions:Institute of Aeroelasticity > Structural Dynamics and System Identification
Deposited By: Soal, Dr Keith Ian
Deposited On:11 Dec 2019 17:49
Last Modified:11 Dec 2019 17:49

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.