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Uncertainty Analysis and Experimental Validation of Identifying the Governing Equation of an Oscillator Using Sparse Regression

Ren, Yaxiong ; Adams, Christian ; Melz, Tobias (2022):
Uncertainty Analysis and Experimental Validation of Identifying the Governing Equation of an Oscillator Using Sparse Regression. (Publisher's Version)
In: Applied Sciences, 12 (2), MDPI, e-ISSN 2076-3417,
DOI: 10.26083/tuprints-00020522,
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Item Type: Article
Origin: Secondary publication DeepGreen
Status: Publisher's Version
Title: Uncertainty Analysis and Experimental Validation of Identifying the Governing Equation of an Oscillator Using Sparse Regression
Language: English
Abstract:

In recent years, the rapid growth of computing technology has enabled identifying mathematical models for vibration systems using measurement data instead of domain knowledge. Within this category, the method Sparse Identification of Nonlinear Dynamical Systems (SINDy) shows potential for interpretable identification. Therefore, in this work, a procedure of system identification based on the SINDy framework is developed and validated on a single-mass oscillator. To estimate the parameters in the SINDy model, two sparse regression methods are discussed. Compared with the Least Squares method with Sequential Threshold (LSST), which is the original estimation method from SINDy, the Least Squares method Post-LASSO (LSPL) shows better performance in numerical Monte Carlo Simulations (MCSs) of a single-mass oscillator in terms of sparseness, convergence, identified eigenfrequency, and coefficient of determination. Furthermore, the developed method SINDy-LSPL was successfully implemented with real measurement data of a single-mass oscillator with known theoretical parameters. The identified parameters using a sweep signal as excitation are more consistent and accurate than those identified using impulse excitation. In both cases, there exists a dependency of the identified parameter on the excitation amplitude that should be investigated in further research.

Journal or Publication Title: Applied Sciences
Volume of the journal: 12
Issue Number: 2
Publisher: MDPI
Collation: 21 Seiten
Uncontrolled Keywords: SINDy-LSPL, sparse regression, system identification, vibration, uncertainty analysis
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 16 Department of Mechanical Engineering > Research group System Reliability, Adaptive Structures, and Machine Acoustics (SAM)
Date Deposited: 13 Apr 2022 11:11
Last Modified: 13 Apr 2022 11:11
DOI: 10.26083/tuprints-00020522
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-205226
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20522
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