LAPSE:2023.13276
Published Article

LAPSE:2023.13276
Force Identification from Vibration Data by Response Surface and Random Forest Regression Algorithms
March 1, 2023
Abstract
Several dynamic projects and fault diagnosis of mechanical structures require the knowledge of the acting external forces. However, the measurement of such forces is often difficult or even impossible; in such cases, an inverse problem must be solved. This paper proposes a force identification method that uses the response surface methodology (RSM) based on central composite design (CCD) in conjunction with a random forest regression algorithm. The procedure initially required the finite element modal model of the forced structure. Harmonic analyses were then performed with varied parameters of forces, and RSM generated a dataset containing the values of amplitude, frequency, location of forces, and vibration acceleration at several points of the structure. The dataset was used for training and testing a random forest regression model for the prediction of any location, amplitude, and frequency of the force to be identified with information on only the vibration acquisition at certain points of the structure. Numerical results showed excellent accuracy in identifying the force applied to the structure.
Several dynamic projects and fault diagnosis of mechanical structures require the knowledge of the acting external forces. However, the measurement of such forces is often difficult or even impossible; in such cases, an inverse problem must be solved. This paper proposes a force identification method that uses the response surface methodology (RSM) based on central composite design (CCD) in conjunction with a random forest regression algorithm. The procedure initially required the finite element modal model of the forced structure. Harmonic analyses were then performed with varied parameters of forces, and RSM generated a dataset containing the values of amplitude, frequency, location of forces, and vibration acceleration at several points of the structure. The dataset was used for training and testing a random forest regression model for the prediction of any location, amplitude, and frequency of the force to be identified with information on only the vibration acquisition at certain points of the structure. Numerical results showed excellent accuracy in identifying the force applied to the structure.
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Keywords
finite element method, force identification, harmonic analysis, random forest regression, response surface methodology
Subject
Suggested Citation
Setúbal FADN, Custódio Filho SDS, Soeiro NS, Mesquita ALA, Nunes MVA. Force Identification from Vibration Data by Response Surface and Random Forest Regression Algorithms. (2023). LAPSE:2023.13276
Author Affiliations
Setúbal FADN: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil [ORCID]
Custódio Filho SDS: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil [ORCID]
Soeiro NS: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil
Mesquita ALA: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil [ORCID]
Nunes MVA: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil [ORCID]
Custódio Filho SDS: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil [ORCID]
Soeiro NS: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil
Mesquita ALA: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil [ORCID]
Nunes MVA: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil [ORCID]
Journal Name
Energies
Volume
15
Issue
10
First Page
3786
Year
2022
Publication Date
2022-05-20
ISSN
1996-1073
Version Comments
Original Submission
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PII: en15103786, Publication Type: Journal Article
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LAPSE:2023.13276
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https://doi.org/10.3390/en15103786
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Mar 1, 2023
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