4.6 Article

Estimating the forcing function in a mechanical system by an inverse calibration method

Journal

JOURNAL OF VIBRATION AND CONTROL
Volume 28, Issue 21-22, Pages 3352-3363

Publisher

SAGE PUBLICATIONS LTD
DOI: 10.1177/10775463211031053

Keywords

Inverse problem; force identification; integral equation; regularization; frequency domain; cross-correlation

Funding

  1. National Science Foundation [NSF-CBET2031808]
  2. Mechanical & Aerospace Engineering Departmental Scholarship through the University of Tennessee Department of Mechanical, Aerospace, and Biomedical Engineering

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This article proposes a novel calibration-based integral formulation for resolving the forcing function in a mass-spring-damper system, applicable to various mechanical systems, and achieved through mathematical modeling and frequency domain analysis.
This article proposes and demonstrates a calibration-based integral formulation for resolving the forcing function in a mass-spring-damper system, given either displacement or acceleration data. The proposed method is novel in the context of vibrations, being thoroughly studied in the field of heat transfer. The approach can be expanded and generalized further to multi-variable systems associated with machine parts, vehicle suspensions, translational and rotational systems, gear systems, etc. when mathematically described by a system of constant property, linear, time-invariant ordinary differential equations. The analytic approach and subsequent numerical reconstruction of the forcing function is based on resolving a parameter-free inverse formulation for the equation(s) of motion. The calibration approach is formulated in the frequency domain and takes advantage of several observations produced by the dimensionality reduction leading to an algebratized system involving an input-output relationship and a transfer function possessing all the system parameters. The transfer function is eliminated in lieu of experimental data, from a calibration effort, thus leading to a reduction of systematic errors. These parameter-free, reduced systematic error aspects are the distinct and novel advantages of the proposed method. A first-kind Volterra integral equation is formed containing only the unknown forcing function and experimental data. As with all ill-posed problems, regularization must be introduced for system stabilization. A future-time technique is instituted for forming a family of predictions based on the chosen regularization parameter. The optimal regularization parameter is estimated using a combination of phase-plane analysis and cross-correlation principles. Finally, a numerical simulation is performed verifying the proposed approach.

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