基于机器学习的随机非线性多自由度动力系统数字孪生
Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system
摘要：
数字孪生技术的潜力是巨大的，特别是在基础设施、航空航天和汽车领域。然而，该技术的实际实施并没有达到预期的速度，特别是因为缺乏特定于应用的细节。针对随机非线性多自由度动力系统，提出了一种新的数字孪生框架。本文提出的方法战略性地将问题分解为两个时间尺度--（a）控制系统动态的快速时间尺度和（b）控制系统退化的慢速时间尺度。拟议的数字孪生模型有四个组成部分-（a）基于物理的名义模型（低保真），（b）贝叶斯过滤算法，（c）监督机器学习算法，（d）预测未来反应的高保真模型。采用基于物理的标称模型结合贝叶斯滤波的方法进行参数状态估计，并采用有监督的机器学习算法学习参数的时间演化。虽然所提出的框架可以与贝叶斯滤波和机器学习算法的任何选择一起使用，但我们建议使用无迹卡尔曼滤波器和高斯过程。所提出的方法的性能说明使用两个例子。结果表明，所提出的数字孪生框架的适用性和优异的性能。
英文摘要：
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.
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