摘要
为了满足生产需要,对轮毂端径跳动量及最低点检测数据处理算法进行了研究。提出基于Kalman滤波的自适应数据融合方法,此算法可以根据标准化后的采样数据与三次B样条近似拟合算法得到的平滑后的数据点采用自适应方法计算各个数据点的不同权重系数最终得到融合后的数据;根据转换公式计算得到各面的端径跳动量差值及最低点角度。实验结果表明该算法数据处理的有效性及良好的鲁棒性。同时开发了轮毂检测系统,系统在轮毂以90度/s的情况下转动一圈得到测量数据后经过系统软硬件处理可使检测精度达到±3度以内,在测量时间及精度方面满足了客户技术文件要求,达到了满意的效果。
In order to meet the needs of production,this paper studies the data processing algorithm for wheel hub end diameter runout and minimum point detection.An adaptive data fusion method based on Kalman filtering is proposed.This algorithm could use the adaptive method to calculate the different weight coefficients of each data point according to the standardized sampling data and the smoothed data points obtained by the cubic B-spline approximate fitting algorithm,so that the fused data was obtained.Then the difference of the end diameter runout of each surface and the lowest point angle were calculated according to the conversion formula.The experimental results show that the algorithm is effective and robust in data processing.We also developed a wheel hub detection system.This system could obtain the measurement data by rotating the hub at 90 degrees per second.After the system software and hardware processing,the detection accuracy could reach±3 degrees,which satisfied the requirements of customer’s technical documents in terms of measurement time and accuracy and achieved satisfactory results.
作者
南雷光
马庆增
李文龙
成巍
Nan Leiguang;Ma Qingzeng;Li Wenlong;Cheng Wei(Laser Research Institute,Qilu University of Technology(Shandong Academy of Sciences),Jining 272017,Shandong,China)
出处
《计算机应用与软件》
北大核心
2023年第2期102-105,共4页
Computer Applications and Software
基金
山东省军民科技融合项目(2019JMRH0411)
山东省重大科技创新工程资助项目(2019JZZY010444)。
关键词
轮毂
端径跳动量
最低点
检测
定位
测量时间
精度
Wheel hub
End diameter jump value
Lowest point
Detection
Positioning
Measurement time
Accuracy
