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

Soal, Keith Ian und Govers, Yves und Bienert, Jörg und 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.

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

Kurzfassung

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.

elib-URL des Eintrags:https://elib.dlr.de/130041/
Dokumentart:Zeitschriftenbeitrag
Titel:System identification and tracking using a statistical model and a Kalman filter
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Soal, Keith IanKeith.Soal (at) dlr.dehttps://orcid.org/0000-0002-5132-6823NICHT SPEZIFIZIERT
Govers, YvesYves.Govers (at) dlr.dehttps://orcid.org/0000-0003-2236-596XNICHT SPEZIFIZIERT
Bienert, JörgJoerg.Bienert (at) thi.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Bekker, Anriëtteannieb (at) sun.ac.zaNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:9 Mai 2019
Erschienen in:Mechanical Systems and Signal Processing
Referierte Publikation:Ja
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:133
DOI:10.1016/j.ymssp.2019.05.011
Verlag:Elsevier
ISSN:0888-3270
Status:veröffentlicht
Stichwörter:system identification, operational modal analysis, ship structures, multivariate statistics, Kalman filter, automated modal parameter selection
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Luftfahrt
HGF - Programmthema:Flugzeuge
DLR - Schwerpunkt:Luftfahrt
DLR - Forschungsgebiet:L AR - Aircraft Research
DLR - Teilgebiet (Projekt, Vorhaben):L - Flugphysik (alt)
Standort: Göttingen
Institute & Einrichtungen:Institut für Aeroelastik > Strukturdynamik und Systemidentifikation
Hinterlegt von: Soal, Dr Keith Ian
Hinterlegt am:11 Dez 2019 17:49
Letzte Änderung:11 Dez 2019 17:49

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