Delaunay三角剖分的汽车螺旋锥齿轮磨损检测
Wear Detection of Automotive Spiral Bevel Gears Based on Delaunay Triangulation
刘怡然 1李丽君 2杜月云1
作者信息
- 1. 商丘职业技术学院交通学院,河南 商丘 476000
- 2. 山东理工大学交通与车辆工程学院,山东 淄博 255049
- 折叠
摘要
由于汽车后桥螺旋锥存在耦合竖向振动,导致齿轮磨损难以精准检测,因此提出Delaunay三角剖分的汽车螺旋锥齿轮磨损检测方法.通过扫描式方法测量齿轮表面的离散数据,对相邻扫描线进行Delaunay三角剖分,完成齿面非特征离散数据分块,实现对齿轮表面区域的全面描述.根据数据分块结果,采用基于Hermite插值的LMD算法,遍历计算三角网格的PF分量幅值,完成齿轮磨损故障的检测.实验结果表明,所提出方法的磨损深度与磨损率检测结果与实测结果基本一致,并且能够对齿轮磨损区域面积进行有效检测,检测精度最高达到98.7%.因此,说明所提出方法能够对齿轮磨损进行有效的检测.
Abstract
Due to the coupled vertical vibration of the automotive rear axle spiral bevel gear,it is difficult to accurately detect gear wear.Therefore,a Delaunay triangulation method for automotive spiral bevel gear wear detection is proposed.By using a scanning method to measure discrete data on the gear surface,Delaunaytriangulation is performed on adjacent scan lines to com-plete non feature discrete data segmentation of the gear surface,achieving a comprehensive description of the gear surface area.Based on the data segmentation results,the LMD algorithm based on Hermite interpolation is used to traverse and calculate the amplitude of the PF component of the triangular mesh,and complete the detection of gear wear faults.The experimental results show that the wear depth and wear rate detection results of the proposed method are basically consistent with the measured results,and can effectively detect the area of gear wear area,with a detection accuracy of up to 98.7%.Therefore,it indicates that the proposed method can effectively detect gear wear.
关键词
Delaunay三角剖分/汽车螺旋锥齿轮/磨损检测/非特征离散数据分块Key words
Delaunay Triangulation/Automobile Spiral Bevel Gear/Wear Detection/Non Characteristic Discrete Data Block引用本文复制引用
基金项目
河南省高等学校重点研究项目(21A880015)
河南省高等教育教学改革研究项目(2021SJGLX771)
出版年
2024
NETL
NSTL
维普
万方数据