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Experimental and operational modal analysis: Automated system identification for safety-critical applications

Volkmar, Robin und Soal, Keith Ian und Govers, Yves und Böswald, Marc (2022) Experimental and operational modal analysis: Automated system identification for safety-critical applications. Mechanical Systems and Signal Processing (MSSP), 183 (109658). Elsevier. doi: 10.1016/j.ymssp.2022.109658. ISSN 0888-3270.

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Kurzfassung

Safety-critical applications like the evaluation of aeroelastic stability during aircraft flight require modal parameters identified with high accuracy. Promising methods of automated modal identification exist. Nevertheless, these methods are not yet chosen for safety-critical applicaions. The reason is either insufficient accuracy of modal parameters or significant adaptions for each individual application. In this work, a new method is presented that not only enables fully automated modal analysis, but also learns an optimal way to analyze the data in a supervised manner. Based on the result of a single manual modal analysis, the self-learning method finds optimal parameters for the automated analysis. In an iterative process, new analysis parameters are chosen by Bayesian Optimization with a Gaussian Process as surrogate model and Expected Improvement as the acquisition function. With these parameters, the method can analyze additional datasets as accurately as a manual expert. The presented method is evaluated on ground vibration test data (i.e., experimental modal analysis) as well as flight vibration data (i.e., operational modal analysis) of an aircraft structure. In contrast to previous methods, the presented method can be easily used for various modal tests, since it can learn by itself to perform optimally with respect to a specific target function like for example the one provided in this work. Due to its robustness, the method is promising also for industrial test cases and safety-critical applications.

elib-URL des Eintrags:https://elib.dlr.de/187833/
Dokumentart:Zeitschriftenbeitrag
Titel:Experimental and operational modal analysis: Automated system identification for safety-critical applications
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Volkmar, RobinRobin.Volkmar (at) dlr.dehttps://orcid.org/0000-0002-5920-0686NICHT SPEZIFIZIERT
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
Böswald, MarcMarc.Boeswald (at) dlr.dehttps://orcid.org/0000-0001-8260-8623NICHT SPEZIFIZIERT
Datum:August 2022
Erschienen in:Mechanical Systems and Signal Processing (MSSP)
Referierte Publikation:Ja
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:183
DOI:10.1016/j.ymssp.2022.109658
Verlag:Elsevier
ISSN:0888-3270
Status:veröffentlicht
Stichwörter:Automated modal analysis, Parametric system identification, Bayesian Optimization, Supervised learning, Experimental modal analysis, Operational modal analysis
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Luftfahrt
HGF - Programmthema:Komponenten und Systeme
DLR - Schwerpunkt:Luftfahrt
DLR - Forschungsgebiet:L CS - Komponenten und Systeme
DLR - Teilgebiet (Projekt, Vorhaben):L - Flugzeugsysteme
Standort: Göttingen
Institute & Einrichtungen:Institut für Aeroelastik > Strukturdynamik und Systemidentifikation
Hinterlegt von: Volkmar, Robin
Hinterlegt am:22 Aug 2022 15:02
Letzte Änderung:22 Aug 2022 15:02

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