东北大学学报:自然科学版 ›› 2019, Vol. 40 ›› Issue (3): 360-364.DOI: 10.12068/j.issn.1005-3026.2019.03.011

• 机械工程 • 上一篇    下一篇

汽车零件磨损渐变可靠性分析

杨周1, 胡全全1, 张义民2, 郭丙帅1   

  1. (1.东北大学 机械工程与自动化学院, 辽宁 沈阳110819; 2.沈阳化工大学 机械工程学院, 辽宁 沈阳 110142)
  • 收稿日期:2018-01-02 修回日期:2018-01-02 出版日期:2019-03-15 发布日期:2019-03-08
  • 通讯作者: 杨周
  • 作者简介:杨周(1979-),女,辽宁沈阳人,东北大学副教授; 张义民(1958-),男,吉林长春人,沈阳化工大学教授,博士生导师.
  • 基金资助:
    国家自然科学联合基金资助项目 (U1710119);辽宁省联合基金资助项目 (U1708254).

Reliability Analysis of Wear Gradient of Automobile Parts

YANG Zhou1, HU Quan-quan1, ZHANG Yi-min2, GUO Bing-shuai1   

  1. 1.School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China; 2.College of Mechanical Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China.
  • Received:2018-01-02 Revised:2018-01-02 Online:2019-03-15 Published:2019-03-08
  • Contact: YANG Zhou
  • About author:-
  • Supported by:
    -

摘要: 为研究车辆零件在磨损作用影响下的可靠性问题,将零件结构尺寸最大磨损量考虑到渐变可靠性模型中,得到随时间变化的可靠性状态方程的数学模型,并利用二阶矩和随机摄动法得出动态可靠性指标,进而获得零部件的渐变可靠性及灵敏度的定量分析结果.通过算例与蒙特卡洛相结合验证了该方法的有效性,拟合得到了零部件磨损渐变可靠性分布规律曲线和灵敏度曲线,很好地反映了零部件几何尺寸在磨损状态下危险截面的可靠性变化情况,为车辆零件的磨损渐变可靠性研究提供了理论参考.

关键词: 车辆零件, 磨损, 渐变可靠性, 蒙特卡洛, 灵敏度

Abstract: To study the reliability of automobile parts under the influence of abrasion, the biggest wear loss is taken into account in the gradual changing reliability model and the model of reliability state with the changing of time is obtained. Then, the secondary moment and the random perturbation method are adopted to give a quantitative analysis of the gradual changing reliability and the sensitivity. Finally, the effectiveness of the proposed method is confirmed by an example and the Monte Carlo. The fitting curves of the regularities of distribution of reliability and of sensitivity with abrasion are obtained, which can well reflect the change of reliability of dangerous sections of automobile parts in wear conditions and provide references for the gradual changing reliability with abrasion.

Key words: automobile parts, abrasion, gradual changing reliability, Monte Carlo, sensitivity

中图分类号: