Uncertainty Estimation of the Subdivision Method of Calibration Results on an Automatic Mass Comparator
Yi Su, Y. Fu, Zhong-qi Xiong
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引用次数: 1
Abstract
According to OIML R111, for determining the conventional mass, there are two methods: the subdivision, and the direct comparison. Comparing with the direct comparison method, the functional relationship in the subdivision/multiplication method is very complicated. Thirteen calculation equations are used to provide an appropriate adjustment calculation so as to avoid propagating errors. The effect of the correlations cannot be ignored during the uncertainty estimation. This manuscript took a set of mg weights as an example, and focused on the uncertainty estimation of the subdivision method of calibration. The uncertainty components were the reference weight uncertainty, the uncertainty of the weighing process, the air buoyancy uncertainty, and the uncertainty of mass comparators, etc. According to the OIML R111 Annex C and the EA-4/02, the uncertainty components were evaluated either by the Type A method or by the Type B method. With fully considering the covariance of the components, the uncertainty of mass calibration in subdivision method was properly estimated. Functional Relationships During the measurement, the uncertainty is a parameter which reasonably characterizes the dispersion of the measured result.With the functional relationship M = f(m1, m2, ..., mn ) in calibrations, output quantity M is related to a number of input quantities mi (i = 1, 2 ,..., n). The mathematical model represents the evaluation methods and the measurement procedure. It also reflects the relationship between input quantities mi and output quantity M. Routinely, there is only one analytical expression in the calibration, but in the subdivision method there are a group of equations with the corrections and the corresponding correction factors. The correlation between input components is also considered. Therefore, the relationships in the subdivision method are not explicitly written down as one function. In this manuscript the relationship in the subdivision method is given by two functions: i i j m m m m (1) * ( , ) j j i m f m m (2) Where, Δmi: the difference in conventional mass between a set of test weights and a reference weight with the same nominal value, i=(1~13); m∑mi: the sum of the conventional mass of the dissemination weights; mj : the conventional mass of the reference 1 g weight or the single test weight in every dissemination group; mj*: the conventional mass of the reference 1 g weigh or the single test weight in last dissemination group; Take 1 mg to 500 mg weight as an example. Table 1 shows the functional relationships in subdivision method. International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019) Copyright © 2019, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Intelligent Systems Research, volume 168
自动质量比较器标定结果细分方法的不确定度估计
根据OIML R111,常规质量的确定有两种方法:细分法和直接比较法。与直接比较法相比,细分/乘法法中的函数关系非常复杂。采用13个计算方程进行适当的平差计算,避免了传播误差。在不确定性估计中,相关性的影响是不可忽视的。本文以一组mg权重为例,重点研究了标定细分方法的不确定度估计。不确定度包括参比重量不确定度、称量过程不确定度、空气浮力不确定度、质量比较器不确定度等。根据OIML R111附录C和EA-4/02,采用A类方法或B类方法对不确定度分量进行评估。在充分考虑各分量协方差的情况下,对细分法质量标定的不确定度进行了合理估计。在测量过程中,不确定度是一个合理表征测量结果离散度的参数。函数关系M = f(m1, m2,…), mn)在校准中,输出量M与若干输入量mi (i = 1,2,…数学模型代表了评价方法和测量过程。它也反映了输入量mi和输出量m之间的关系。通常,在校准中只有一个解析表达式,但在细分方法中,有一组方程,其中包含了校正和相应的校正因子。还考虑了输入分量之间的相关性。因此,细分方法中的关系没有明确地写成一个函数。在本文中,细分方法中的关系由两个函数给出:i i j m m m m m(1)* (,)j j i m m m(2)式中,Δmi:一组试验权值与具有相同标称值的参考权值之间的常规质量差,i=(1~13);M∑mi:传播权的常规质量之和;Mj:每个传播组参考1 g重量或单个试验重量的常规质量;Mj *:最后一次传播组参考1 g重或单次试验重的常规质量;以1毫克至500毫克的重量为例。表1显示了细分方法中的函数关系。建模、分析、仿真技术与应用国际会议(MASTA 2019)版权所有©2019,作者。亚特兰蒂斯出版社出版。这是一篇基于CC BY-NC许可(http://creativecommons.org/licenses/by-nc/4.0/)的开放获取文章。智能系统研究进展,第168卷
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