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基于深度学习神经网络的自动驾驶座椅丝杠系统摩擦噪声机理分析及动态不稳定性预测

Analysis of Friction Noise Mechanism in Lead Screw System of Autonomous Vehicle Seats and Dynamic Instability Prediction Based on Deep Neural Network.

机构信息

AI & Mechanical System Center, Institute for Advanced Engineering, Youngin-si 17180, Republic of Korea.

出版信息

Sensors (Basel). 2023 Jul 5;23(13):6169. doi: 10.3390/s23136169.

Abstract

This study investigated the squeal mechanism induced by friction in a lead screw system. The dynamic instability in the friction noise model of the lead screw was derived through a complex eigenvalue analysis via a finite element model. A two degree of freedom model was described to analyze the closed solutions generated in the lead screw, and the friction noise sensitivity was examined. The analysis showed that the main source of friction noise in the lead screw was the bending mode pair, and friction-induced instability occurred when the ratio of the stiffness of the bending pair modes was 0.9-1. We also built an architecture to predict multiple outputs from a single model using deep neural networks and demonstrated that friction-induced instability can be predicted by deep neural networks. In particular, instability with nonlinearity was predicted very accurately by deep neural networks with a maximum absolute difference of about 0.035.

摘要

本研究探讨了丝杠系统中摩擦引起的啸叫机制。通过有限元模型的复特征值分析,推导出丝杠摩擦噪声模型中的动态不稳定性。描述了一个两自由度模型来分析丝杠中产生的封闭解,并检验了摩擦噪声的敏感性。分析表明,丝杠中摩擦噪声的主要来源是弯曲模态对,当弯曲模态对的刚度比为 0.9-1 时,摩擦会引起不稳定性。我们还构建了一个架构,使用深度神经网络从单个模型预测多个输出,并证明摩擦引起的不稳定性可以通过深度神经网络来预测。特别是,深度神经网络可以非常准确地预测具有非线性的不稳定性,最大绝对差值约为 0.035。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b518/10346787/2f9733951620/sensors-23-06169-g001.jpg

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