Stochastic Modeling and Analysis of Automotive Wire Harness Based on Machine Learning and Polynomial Chaos Method
Stochastic Modeling and Analysis of Automotive Wire Harness Based on Machine Learning and Polynomial Chaos Method
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DOI：
10.1109/emceurope51680.2022.9901033
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
T. Sekine;S. Usuki;K. Miura
中科院分区：
文献类型：
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作者：
T. Sekine;S. Usuki;K. Miura
提出了一种基于机器学习技术和多项式混沌（PC）方法的汽车线束随机行为建模与分析方法。在本研究中，我们假设汽车线束是一束导线以上的导体平面，其行为可以表示为随机传输线方程。该方法首先建立与单位长度（p.u.l.）通过机器学习技术的方式来确定参数。然后，包括回归模型的随机传输线方程近似使用正交多项式通过PC制定。由于回归模型将导丝的几何和形状参数与p.u.l.参数，PC膨胀系数可以有效地计算。我们采用三种类型的回归模型，并比较它们的性能研究所提出的方法。
This paper proposes a method based on machine learning technique and polynomial chaos (PC) method to model and analyze the stochastic behavior of an automotive wire harness. In this research, we assume that the automotive wire harness is a bundle of wires above a conductor plane, and its behavior can be represented by stochastic transmission line equations. First, the proposed method constructs the regression models related to per-unit-length (p.u.l.) parameters by means of a machine learning technique. Then, the stochastic transmission line equations including the regression models are approximated using orthonormal polynomials through a PC formulation. Since the regression models correlate the geometric and shape parameters of the wires and the p.u.l. parameters, PC expansion coefficients can efficiently be calculated. We adopt three types of regression models and compare them to investigate the performance of the proposed method.