Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system

TL;DR

This paper introduces a novel digital twin framework for stochastic nonlinear multi-degree of freedom systems, combining physics-based models, Bayesian filtering, and machine learning to improve prediction accuracy and system monitoring.

Contribution

It proposes a new digital twin architecture that decouples system dynamics and degradation processes, integrating multiple modeling techniques for enhanced performance.

Findings

Framework demonstrates high accuracy in system response prediction

Effective in capturing stochastic nonlinear system behavior

Shows promising results in two illustrative examples

Abstract

The potential of digital twin technology is immense, specifically in the infrastructure, aerospace, and automotive sector. However, practical implementation of this technology is not at an expected speed, specifically because of lack of application-specific details. In this paper, we propose a novel digital twin framework for stochastic nonlinear multi-degree of freedom (MDOF) dynamical systems. The approach proposed in this paper strategically decouples the problem into two time-scales -- (a) a fast time-scale governing the system dynamics and (b) a slow time-scale governing the degradation in the system. The proposed digital twin has four components - (a) a physics-based nominal model (low-fidelity), (b) a Bayesian filtering algorithm a (c) a supervised machine learning algorithm and (d) a high-fidelity model for predicting future responses. The physics-based nominal model combined with Bayesian filtering is used combined parameter state estimation and the supervised machine learning algorithm is used for learning the temporal evolution of the parameters. While the proposed framework can be used with any choice of Bayesian filtering and machine learning algorithm, we propose to use unscented Kalman filter and Gaussian process. Performance of the proposed approach is illustrated using two examples. Results obtained indicate the applicability and excellent performance of the proposed digital twin framework.