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首页> 外文期刊>Journal of Sound and Vibration >Calculation on the radiation noise of ceramic ball bearings based on dynamic model considering nonlinear contact stiffness and damping
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Calculation on the radiation noise of ceramic ball bearings based on dynamic model considering nonlinear contact stiffness and damping

机译:基于非线性接触刚度和阻尼的动态模型基于动态模型的陶瓷滚珠轴承辐射噪声的计算

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摘要

Radiation noise (RN) is an important parameter for representing the characteristics of a bearing. By simplifying a ball bearing as a mass-spring-damping system, the differential equations for the vibration of an angular contact ceramic ball bearing (ACCBB) are established from the view of energy conservation considering nonlinear contact stiffness and damping. Then, the RN model of the bearing is constructed by Helmholtz's acoustic theory. The bearing noise is calculated using the proposed model, and the corresponding experiments are performed to confirm the model precision. The bearing noise consists of the noise at the characteristic frequency of each component; the rotational frequency is the primary contributor to the bearing noise. The radiation noise first increases with increasing rotational speed and the sound pressure level (SPL) has a high peak value at a critical speed of 28,000 r.min(-1). Then, the noise decreases with increasing rotational speed. The directivity of sound field is discussed at different rotational speed. With increasing the rotational speed, the directivity level (DL) increases and the directivity angle (DA) skews towards the rotation direction. The research provides a theoretical basis to analyze the RN generated by ACCBBs at different rotational speeds. (C) 2020 Elsevier Ltd. All rights reserved.
机译:辐射噪声(RN)是表示轴承特性的重要参数。通过将滚珠轴承简化为质量弹簧阻尼系统,从考虑非线性接触刚度和阻尼的节能视图建立了角接触陶瓷滚珠轴承(ACCBB)的振动的微分方程。然后,轴承的RN模型由Helmholtz的声学理论构成。使用所提出的模型计算轴承噪声,并进行相应的实验以确认模型精度。轴承噪声由每个部件的特征频率的噪声组成;旋转频率是轴承噪声的主要贡献者。辐射噪声首先随着旋转速度的增加而增加,声压水平(SPL)具有高峰值,以28,000 r.min(-1)的临界速度。然后,噪声随着旋转速度的增加而降低。声场的方向性以不同的转速讨论。随着旋转速度的增加,方向性水平(DL)增加,并且方向性角度(DA)朝向旋转方向倾斜。该研究提供了一种理论依据,用于分析由不同的转速产生的ACCBB产生的RN。 (c)2020 elestvier有限公司保留所有权利。

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