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.
Dieses Archiv kann nicht den Volltext zur Verfügung stellen.
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: |
| ||||||||||||||||||||
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 |
Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags