识别具有不连续非线性的机械系统的切换高斯过程潜力模型

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
L. Marino, A. Cicirello
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引用次数: 2

摘要

摘要提出了一种识别机械系统中由摩擦接触产生的不连续和非光滑非线性力的方法,这种力可以用单自由度模型近似。为了处理这些非线性在动态响应中引入的急剧变化和多种运动状态,将部分已知的基于物理的模型和系统对已知输入力响应的噪声测量结合在切换高斯过程潜在力模型(GPLFM)中。在这个灰盒框架中,使用多个高斯过程来模拟不同运动状态下的未知非线性力,重置模型可以生成不连续点。通过使用滤波和平滑技术来切换线性动力系统,可以推断出系统的状态、非线性力和状态转换。将所提出的开关式GPLFM应用于一个模拟干摩擦振荡器和一个由带有铜-钢触点的单层框架组成的实验装置。对于不同的非线性和不连续的摩擦力,得到了很好的结果:(i)接触中的法向载荷幅值;(ii)测量噪声水平,以及(iii)数据集中的样本数量。此外,识别的状态、摩擦力和运动状态序列用于评估:(1)不确定的系统参数;(2)摩擦力-速度关系;(3)静摩擦力。对不连续非线性力的正确识别和对其预测中任何剩余不确定性的量化,使准确的正演模型能够预测系统对不同输入力的响应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearity
Abstract An approach for the identification of discontinuous and nonsmooth nonlinear forces, as those generated by frictional contacts, in mechanical systems that can be approximated by a single-degree-of-freedom model is presented. To handle the sharp variations and multiple motion regimes introduced by these nonlinearities in the dynamic response, the partially known physics-based model and noisy measurements of the system’s response to a known input force are combined within a switching Gaussian process latent force model (GPLFM). In this grey-box framework, multiple Gaussian processes are used to model the unknown nonlinear force across different motion regimes and a resetting model enables the generation of discontinuities. The states of the system, nonlinear force, and regime transitions are inferred by using filtering and smoothing techniques for switching linear dynamical systems. The proposed switching GPLFM is applied to a simulated dry friction oscillator and an experimental setup consisting of a single-storey frame with a brass-to-steel contact. Excellent results are obtained in terms of the identified nonlinear and discontinuous friction force for varying: (i) normal load amplitudes in the contact; (ii) measurement noise levels, and (iii) number of samples in the datasets. Moreover, the identified states, friction force, and sequence of motion regimes are used for evaluating: (1) uncertain system parameters; (2) the friction force–velocity relationship, and (3) the static friction force. The correct identification of the discontinuous nonlinear force and the quantification of any remaining uncertainty in its prediction enable the implementation of an accurate forward model able to predict the system’s response to different input forces.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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