Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system
This work addresses a practical bottleneck in digital twin adoption for sectors like infrastructure, aerospace, and automotive, though it appears incremental as it builds on existing methods like unscented Kalman filters and Gaussian processes.
The paper tackles the challenge of implementing digital twin technology for stochastic nonlinear multi-degree-of-freedom dynamical systems by proposing a novel framework that decouples the problem into fast and slow time-scales, using a combination of physics-based models, Bayesian filtering, and supervised machine learning, with results showing excellent performance in two examples.
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.