支持向量机评定同轴度误差测量不确定度

针对目前评定同轴度误差测量不确定度过程复杂,且误差分布未知的情况下很难估计测量结果的概率分布及其不确定度的问题,以现有少量数据为基础,基于支持向量机法求取被测量数据的概率密度,并利用所得概率密度分布进行数值积分,计算其估值和标准不确定度。通过实验结果证明了在小样本数据的条件下,支持向量机法得到的样本的最佳估值和方差的准确度。最后以一个工程实例即机床芯轴的一段阶梯轴的同轴度误差为测量对象进行实测实验,评定其测量不确定度,并对比《测量不确定度表示指南》法、蒙特卡洛法的评定结果,验证了方法的可靠性及准确度。

Evaluation of coaxiality error measurementuncertainty by support vector machine

It is difficult to estimate the probability distribution and uncertainty of measurement results when the process of evaluating theuncertainty of coaxiality error measurement is complex and the error distribution is unknown. Based on a small amount of existing data,the probability density of the measured data is calculated by the support vector machine method (SVM), and the numerical integration ofthe obtained probability density distribution is carried out to calculate its estimation and standard uncertainty. The experimental resultsshow that under the condition of small sample data, the best estimation and the accuracy of variance of the samples obtained by SVM areproved. Finally, the coaxiality error of the machine tool core shaft is taken as the experimental object, and the mentioned results areused as the experimental object. Methods to calculate the measurement uncertainty and the measurement uncertainty representation guide(GUM), Monte Carlo method (MCM) calculation results are compared to verify the simplicity, reliability and accuracy of the method.