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

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

Volkmar, Robin and Soal, Keith Ian and Govers, Yves and 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.

Full text not available from this repository.


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.

Item URL in elib:https://elib.dlr.de/187833/
Document Type:Article
Title:Experimental and operational modal analysis: Automated system identification for safety-critical applications
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Volkmar, RobinUNSPECIFIEDhttps://orcid.org/0000-0002-5920-0686UNSPECIFIED
Soal, Keith IanUNSPECIFIEDhttps://orcid.org/0000-0002-5132-6823UNSPECIFIED
Govers, YvesUNSPECIFIEDhttps://orcid.org/0000-0003-2236-596XUNSPECIFIED
Böswald, MarcUNSPECIFIEDhttps://orcid.org/0000-0001-8260-8623UNSPECIFIED
Date:August 2022
Journal or Publication Title:Mechanical Systems and Signal Processing (MSSP)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Keywords:Automated modal analysis, Parametric system identification, Bayesian Optimization, Supervised learning, Experimental modal analysis, Operational modal analysis
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Components and Systems
DLR - Research area:Aeronautics
DLR - Program:L CS - Components and Systems
DLR - Research theme (Project):L - Aircraft Systems
Location: Göttingen
Institutes and Institutions:Institute of Aeroelasticity > Structural Dynamics and System Identification
Deposited By: Volkmar, Robin
Deposited On:22 Aug 2022 15:02
Last Modified:22 Aug 2022 15:02

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.