Force Identification from Vibration Data by Response Surface and Random Forest Regression Algorithms

Fábio Antônio do Nascimento Setúbal, Sérgio de Souza Custódio Filho, Newton Sure Soeiro, Alexandre Luiz Amarante Mesquita and Marcus Vinicius Alves Nunes
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Fábio Antônio do Nascimento Setúbal: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil
Sérgio de Souza Custódio Filho: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil
Newton Sure Soeiro: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil
Alexandre Luiz Amarante Mesquita: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil
Marcus Vinicius Alves Nunes: Institute of Technology, Federal University of Pará, Belém 66075-110, PA, Brazil

Energies, 2022, vol. 15, issue 10, 1-15

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.

Keywords: response surface methodology; force identification; finite element method; harmonic analysis; random forest regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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