Nonlinear ensemble gray and black-box system identification of friction induced vibrations in slender rotating structures

Due to the nonlinear bit-rock and drill-string-borehole interactions, the modeling and analysis of drill string dynamics are a challenging work. These slender structures are subjected to torsional, lateral, and axial vibration modes, which are generally prejudicial to the drilling process. For this reason, proper modeling of the system dynamics is necessary to optimize drilling performance. In the present paper, we focus on the torsional dynamics and model of an experimental system subjected to friction torque. An ensemble approach, combining gray and black-box modeling techniques, is used to calibrate some of the dynamical system parameters, particularly those related to friction. This combination is chosen with the aim of improving the prediction accuracy of a pure gray-box approach while retaining interpretability in the dynamic responses. The present paper compares four well-known friction models with increasing levels of complexity using experimental data. It is shown that the ensemble model proposed can improve the gray-box by up to 92.68% in terms of prediction errors, by adding a non-physical layer to it. The present analysis demonstrates that better results can be obtained by using an ensemble of gray and black-box identification techniques for friction modeling and simulation, aiming at improving data-driven model construction of complex torsional dynamics in slender structures.

Automated summary

The modeling and analysis of drill string dynamics are challenging due to the interactions between nonlinear bit-rock and drill-string-borehole. Torsional, lateral, and axial vibration modes have a negative impact on drilling process, making proper modeling of the system dynamics necessary. This paper focuses on torsional dynamics and model of an experimental system subjected to friction torque, using an ensemble approach to calibrate dynamical system parameters related to friction and improve prediction accuracy.