Identification of parameters in the torsional dynamics of a drilling process through Bayesian statistics

TL;DR

This paper employs Bayesian statistics to estimate parameters in a torsional drilling process model, revealing significant deviations from nominal values and highlighting model deficiencies.

Contribution

It introduces a Bayesian parameter estimation method for a complex drilling system, accounting for measurement uncertainties and assessing model accuracy.

Findings

High deviation of estimated parameters from nominal values

Identification of deficiencies in the current mathematical model

Validation of Bayesian approach for dynamic system analysis

Abstract

This work presents the estimation of the parameters of an experimental setup, which is modeled as a system with three degrees of freedom, composed by a shaft, two rotors, and a DC motor, that emulates a drilling process. A Bayesian technique is used in the estimation process, to take into account the uncertainties and variabilities intrinsic to the measurement taken, which are modeled as a noise of Gaussian nature. With this procedure it is expected to check the reliability of the nominal values of the physical parameters of the test rig. An estimation process assuming that nine parameters of the experimental apparatus are unknown is conducted, and the results show that for some quantities the relative deviation with respect to the nominal values is very high. This deviation evidentiates a strong deficiency in the mathematical model used to describe the dynamic behavior of the experimental apparatus.