基于互信息的特征选择,用于动力系统的反映射参数更新

IF 2.6 2区 工程技术 Q2 MECHANICS
Bas M. Kessels, Rob H. B. Fey, Nathan van de Wouw
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引用次数: 0

摘要

数字孪生系统在其整个运行过程中都应是并始终是物理系统的精确模型代表。为此,我们的目标是以在线方式更新这种模型的(物理上可解释的)参数。为此,我们采用了反映射参数更新(IMPU)方法,利用人工神经网络(ANN)将从测量数据中提取的特征映射到参数估计。这是通过在模拟数据(即已知参数值集和从相应模拟中提取的特征集)上离线训练人工神经网络来实现的。由于可以从模拟时域数据中提取大量特征(和特征类型),因此对特征选择 (FS) 策略进行了研究。这些策略利用特征和参数之间的互信息来选择信息丰富的特征子集。因此,ANN 估算参数的准确性得到了提高,同时 ANN 的训练和推理计算时间也减少了。此外,还采用了基于贝叶斯搜索的超参数调整,以提高方差分析网络的性能,并针对各种 FS 策略优化方差分析网络结构。最后,IMPU 方法被应用于半导体机器的高科技工业应用案例中,对受控物理系统进行闭环测量。该系统在 Simscape 多体环境中被建模为非线性多体模型。结果表明,与手动确定参数值的参考模型相比,使用 IMPU 方法更新的模型能更准确地模拟测量系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Mutual information-based feature selection for inverse mapping parameter updating of dynamical systems

Mutual information-based feature selection for inverse mapping parameter updating of dynamical systems

A digital twin should be and remain an accurate model representation of a physical system throughout its operational life. To this end, we aim to update (physically interpretable) parameters of such a model in an online fashion. Hereto, we employ the inverse mapping parameter updating (IMPU) method that uses an artificial neural network (ANN) to map features, extracted from measurement data, to parameter estimates. This is achieved by training the ANN offline on simulated data, i.e., pairs of known parameter value sets and sets of features extracted from corresponding simulations. Since a plethora of features (and feature types) can be extracted from simulated time domain data, feature selection (FS) strategies are investigated. These strategies employ the mutual information between features and parameters to select an informative subset of features. Hereby, accuracy of the parameters estimated by the ANN is increased and, at the same time, ANN training and inference computation times are decreased. Additionally, Bayesian search-based hyperparameter tuning is employed to enhance performance of the ANNs and to optimize the ANN structure for various FS strategies. Finally, the IMPU method is applied to a high-tech industrial use case of a semi-conductor machine, for which measurements are performed in closed-loop on the controlled physical system. This system is modeled as a nonlinear multibody model in the Simscape multibody environment. It is shown that the model updated using the IMPU method simulates the measured system more accurately than a reference model of which the parameter values have been determined manually.

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来源期刊
CiteScore
6.00
自引率
17.60%
发文量
46
审稿时长
12 months
期刊介绍: The journal Multibody System Dynamics treats theoretical and computational methods in rigid and flexible multibody systems, their application, and the experimental procedures used to validate the theoretical foundations. The research reported addresses computational and experimental aspects and their application to classical and emerging fields in science and technology. Both development and application aspects of multibody dynamics are relevant, in particular in the fields of control, optimization, real-time simulation, parallel computation, workspace and path planning, reliability, and durability. The journal also publishes articles covering application fields such as vehicle dynamics, aerospace technology, robotics and mechatronics, machine dynamics, crashworthiness, biomechanics, artificial intelligence, and system identification if they involve or contribute to the field of Multibody System Dynamics.
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