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

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.

A System for Monitoring and Normative Qualification of Building Structure Vibrations Induced by Nearby Construction Works

“…Another approach to vibration monitoring is predicting object vibrations based on the vibration properties of its selected elements. In [30], only the vibration data at certain points of the structure were selected for analysis. A random forest regression model for predicting any location of the structure, amplitude, and frequency of the force was used [30].…”

“…In [30], only the vibration data at certain points of the structure were selected for analysis. A random forest regression model for predicting any location of the structure, amplitude, and frequency of the force was used [30].…”