Thermal-induced influences considered spindle unit angular contact ball bearing preload determination using embedded fiber Bragg gating sensors

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Distributed Sensor Networks Pub Date : 2022-03-01 DOI:10.1177/15501329221082430
Yanfang Dong, Feifan Chen, M. Qiu
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引用次数: 1

Abstract

As the most important segment of the spindle unit angular contact ball bearing, the preload significantly influences the bearing characteristics. Thus, the thermal-induced preload derived from the thermal expansion of spindle unit components also affects the increase in bearing temperature, stiffness, fatigue life, and ball skidding significantly. However, such preload is hard to monitor and analyze. Thus, in this article, the authors presented a fiber Bragg gating sensor-based structure for the identification of thermal-induced bearing preload. In addition, a bearing total preload control mechanism was designed with an emphasis on its thermal component. Based on the comparison of the shaft and the outer ring deformation temperature increases measured by embedded fiber Bragg gating sensors, the reasonable bearing preload range was achieved based on Hirano’s theory. Finally, the conclusions provide a reference for improving the performance of angular contact ball bearings and reducing the spindle vibration.
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考虑热致影响的主轴单元角接触球轴承预紧力的测定采用嵌入式光纤Bragg门控传感器
作为主轴单元角接触球轴承中最重要的部分,预载荷对轴承特性有很大影响。因此,由主轴单元部件的热膨胀产生的热诱导预载荷也会显著影响轴承温度、刚度、疲劳寿命和滚珠打滑的增加。然而,这种预载荷很难监测和分析。因此,在本文中,作者提出了一种基于光纤布拉格门控传感器的结构,用于识别热感应轴承预载荷。此外,还设计了一种轴承总预载控制机构,重点考虑了其热元件。通过对嵌入式光纤布拉格光栅传感器测量的轴和外圈变形温升的比较,基于平野理论得出了合理的轴承预载范围。最后,研究结论为提高角接触球轴承的性能和降低主轴振动提供了参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.50
自引率
4.30%
发文量
94
审稿时长
3.6 months
期刊介绍: International Journal of Distributed Sensor Networks (IJDSN) is a JCR ranked, peer-reviewed, open access journal that focuses on applied research and applications of sensor networks. The goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in manufacturing, engineering and environmental systems.
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