利用iGPS进行大尺寸空间坐标测量的不确定度评估

iGPS是一种基于激光旋转扫描定位的网络式大尺寸空间坐标测量系统。为分析系统的空间坐标测量不确定度,建立空间坐标测量模型,分析影响系统测量精度的误差源,利用蒙特卡罗法进行了仿真。针对其典型的四站网络测量系统,对系统的测量不确定度进行评估,获得系统的误差分布规律,为iGPS现场大尺寸精密测量系统的具体应用提供有利的支撑。     

基于自适应蒙特卡罗方法的测量不确定度评定

在GUM Sup.1提出基于分布传播的不确定度评定基础上,系统地介绍了自适应蒙特卡罗方法进行不确定度评定的原理和步骤,并以此对线性测量模型和非线性测量模型进行了仿真,得出自适应蒙特卡罗方法对两类测量模型不确定度都有较好的评定效果,同时指出自适应蒙特卡罗方法中仿真次数M和数值容差δ的合理选择都需要进一步研究。     

基于蒙特卡罗方法的平面度测量不确定度评定

介绍利用蒙特卡罗方法进行平面度测量的不确定度评定.首先,根据最小二乘法得到平面度的误差模型,再采用蒙特卡罗仿真方法对测量值进行模拟仿真,从而得到平面度误差的不确定度,然后将评定结果与传统评定方法的结果进行比较.结果表明该方法简便易行.     

wMPS测角不确定度研究

室内空间测量定位系统(wMPS)是一种基于多向定位原理的新型网络式测量系统,为分析系统单站的测角不确定度,建立了角度测量模型,分析了影响系统测角精度的误差源并给出误差的评估方法,利用蒙特卡罗统计法进行了仿真,获得了测角误差的分布.提出一种以多面棱体和平行光管作为调整手段,多齿分度台为角度参考基准的发射站水平角不确定度分...     

非正交轴系全站仪坐标测量系统误差分析技术研究

基于非正交轴系全站仪坐标测量系统的结构特点和测量模型,用数学分析的手段对其进行误差分析和测量不确定度评定。确定系统的主要误差分量是转台旋转角误差和激光跟踪仪测距值误差,并用GUM法评定各分量的不确定度。通过测量模型推导出系统的测量不确定度,并用MATLAB进行仿真,结果表明:当测距值不变时,测量不确定度几乎不受水平角变化的影响,而随着垂直角绝对值的增大而增大,当角度值不变时,测量不确定度随着被测点到视准轴上标定点的距离值增大而增大。实验初步验证了仿真结果的准确性。     

基于多源融合理论的机床圆度误差测量不确定度评定

圆度误差是评价机床加工精度的重要指标.为实现机床圆度误差测量不确定度的评定,对基于球杆仪测量的机床圆度误差的贡献因素及不确定度评定方法进行研究.首先,采用最小二乘法(least sqaure method,LSM)对圆度误差进行评定.然后,基于黑箱理论提出了多源融合误差测量不确定度评定方法.接着,根据球杆仪测量机床圆度...     

智能仪器测量信号功率的不确定度评定模型

针对基于交流采样原理的智能仪器,提出一种新的测量不确定度评定模型。以信号功率测量为例,受硬件条件以及信号频率波动的影响,无法确保同步采样,利用已有测量算法将使测量结果出现误差。将该误差视为系统效应,通过近似处理,提出简单且实用的修正算法。将测量过程中的量化噪声、信号传输中的干扰当作具有已知分布特征的随机变量,利用统计方法,并依据测量不确定度传播定律,评定了经修正算法修正后的测量结果的不确定度。这种先修正系统效应、再评定随机因素造成不确定度的模型,更符合测量过程的实际情况。物理实验和仿真计算均验证了所得结论的有效性。     

温度开关动作误差的蒙特卡罗法不确定度评定

由于温度开关广泛应用于家用电器、工业以及机电等众多行业中,作为一个重要的部件,温度开关是否合格对于设备的性能会有直接的影响。文章通过蒙特卡罗法对温度开关动作误差进行不确定度评定,即介绍了蒙特卡罗方法在温度开关动作误差测量不确定度评定中的应用。近年来蒙特卡罗法被广泛应用于测量不确定度的评定,并且在很多的不确定度评定中会与传统的评定方法进行比较。文章中分析对比蒙特卡罗不确定度评定与传统测量不确定度方法评定的结果。最后做出结论。     

蒙特卡罗方法在微波功率测量不确定度分析中的应用

蒙特卡罗方法是一种统计模拟的方法,近年来国际上将其应用于计量学中的不确定度评定.对蒙特卡罗方法进行了系统的描述,就蒙特卡罗方法在计量中的不确定度分析中的应用进行了详细的论述.采用蒙特卡罗方法对微波功率座的测量问题进行了数学建模及蒙特卡罗仿真,对蒙特卡罗方法进行不确定度分析和评定的原理、步骤进行了阐述.给出了微波功率座校准因子的实测值、仿真值及不确定度评定结果.     

坐标测量机孔径测量的不确定度评定模型研究

以坐标测量机测量孔径为例,阐述测量过程中影响测量结果的不确定度来源,根据测量模型建立孔径测量的GUM法不确定度评定模型;利用对坐标测量机的测量系统量值特性指标分析的方法 ,给出基于量值特性分析法的各标准不确定度分量的评定模型。通过对汽车空调压缩机后缸体的孔径测量,比较两种方法评定的扩展不确定度。实例分析可以看出:对于坐标测量机复杂的非线性测量模型,GUM法在计算灵敏系数时,运算量较大且获得的是近似结果,因此其可操作性不强;量值特性分析法通过对测量系统整体的分析,基于大量的实验数据对测量结果进行测量不确定度评定,其流程和操作性更为便捷、有效。     

自适应蒙特卡洛法评定全站仪测距不确定度

全站仪测距精度的校准需要在标准基线场上进行,由于野外环境不可控和气象条件波动剧烈,因此判断全站仪的测量结果的可靠程度具有重要意义。为了解决全站仪测距不确定度评定模型的非线性和输入量强相关等问题,本文首先采用了自适应蒙特卡洛法进行不确定度评定,然后与GUM的不确定度评定结果进行对比,当测距距离为1 176 m时,自适应蒙特卡洛法评定的不确定度结果为2.2 mm,GUM为2.6 mm,结果显示两种不确定度评定方法的测量结果均在合理预期之内,且自适应蒙特卡洛法评定的不确定度置信区间更窄。自适应蒙特卡洛法结合了大量数据样本和自适应优化仿真次数的优势,不仅对全站仪测距过程中的各项误差源引入的不确定度分量评估更为全面,而且在保证了全站仪测距不确定度评定结果准确的同时,相比于蒙特卡洛法节约了70%的样本数量。     

失配误差的蒙特卡洛分析

针对极限相位法评定失配误差引入的测量不确定度普遍偏大的问题,提出了采用蒙特卡洛法对其评定的新方法。以交替比较法校准功率座为实例,研究了蒙特卡洛法评定失配误差引入的测量不确定度的具体实现方法,并将其得到的结果与极限相位法进行比较。结果表明,蒙特卡洛法更适合用于失配误差引入的测量不确定度进行评定。     

A Bayesian framework for uncertainty quantification of perturbed gamma process based on simulated likelihood

The perturbed gamma process (PGP) has recently been widely used in modeling the noisy degradation data collected from engineering structures and components since it can simultaneously consider the temporal variability of degradation and measurement uncertainty. As a result of the sampling and inspection uncertainty in engineering practice, it is necessary to account for the resulting parameter uncertainty. Meanwhile, the flexibility of the form of measurement error motivates a potential demand for quantifying the model uncertainty and selecting the most fitting error model for the given inspection data. The Bayesian approach is well-suited to quantity the parameter uncertainty induced by imperfect inspection and limited inspection data, but its practical implementation is extremely challenging due to the intractable likelihood function of PGP. In the paper, a novel Bayesian framework for quantifying parameter and model uncertainty of PGP is presented, where the simulated likelihood that is an unbiased estimator generated by Sequential Monte Carlo (SMC) is introduced to overcome the intractable likelihood of PGP. More specifically, an Adaptive Particle Markov chain Monte Carlo (APMCMC) is proposed to perform the Bayesian sampling from the posterior distributions of parameters, achieving the requirement for the quantification of parameter uncertainty. By utilizing the posterior samples from APMCMC, a model selection method based on the Bayes factor is employed to determine the most fitting one from some alternative error models. Finally, two simulation examples are presented to illustrate the efficiency and accuracy of the proposed framework and its applicability is confirmed by a practical case involving the corrosion modeling of a group of pipelines.     

1 on 1损伤阈值测量结果不确定度分析

针对ISO 21254中1 on1损伤阈值测试结果的测量不确定度评估需求,系统性分析测量结果不确定度的主要影响因素.采用公式法分析评定能量密度的测量不确定度以及损伤概率的测量不确定度分量.采用蒙特卡洛方法分析了线性拟合引入的测量不确定度,并对比分析了线性拟合过程中不同残差模型对损伤概率、能量密度线性拟合结果的影响.分析...     

Evaluation of Measurement Uncertainty Based on the Propagation of Distributions Using Monte Carlo Simulation

The uncertainty associated with a value of some quantity is widely recognized throughout scientific disciplines as a quantitative measure of the reliability of that value. In addition, measurement uncertainty is increasingly seen as essential in quality assurance for industry. The Guide to the Expression of Uncertainty in Measurement (GUM) provides internationally agreed recommendations for the evaluation of uncertainties. This paper outlines the current situation of uncertainty evaluation in the context of international norms and arrangements. It describes the basic ideas and concepts that underlie the GUM and serves as a brief tutorial on methods for evaluating measurement uncertainty in a manner consistent with the GUM. It recommends an approach to evaluating measurement uncertainty based on the propagation of distributions using Monte Carlo simulation. An example is presented to illustrate Monte Carlo simulation.     

激光跟踪仪测量曲面的测量不确定度研究

针对激光跟踪仪用于曲面轮廓度测量的不确定度评定问题,在论述了激光跟踪仪的标定和面向任务的测量不确定度的基础上,重点研究了Monte-Carlo模拟方法评价面向任务的不确定度的基本思想,并提出了虚拟激光跟踪仪的概念.最后通过实验研究,验证了采用Monte-Carlo模拟方法评价激光跟踪仪测量曲面轮廓度的不确定度是可行的.     

Measurement Uncertainty of Dew-Point Temperature in a Two-Pressure Humidity Generator

This article describes the measurement uncertainty evaluation of the dew-point temperature when using a two-pressure humidity generator as a reference standard. The estimation of the dew-point temperature involves the solution of a non-linear equation for which iterative solution techniques, such as the Newton?CRaphson method, are required. Previous studies have already been carried out using the GUM method and the Monte Carlo method but have not discussed the impact of the approximate numerical method used to provide the temperature estimation. One of the aims of this article is to take this approximation into account. Following the guidelines presented in the GUM Supplement 1, two alternative approaches can be developed: the forward measurement uncertainty propagation by the Monte Carlo method when using the Newton?CRaphson numerical procedure; and the inverse measurement uncertainty propagation by Bayesian inference, based on prior available information regarding the usual dispersion of values obtained by the calibration process. The measurement uncertainties obtained using these two methods can be compared with previous results. Other relevant issues concerning this research are the broad application to measurements that require hygrometric conditions obtained from two-pressure humidity generators and, also, the ability to provide a solution that can be applied to similar iterative models. The research also studied the factors influencing both the use of the Monte Carlo method (such as the seed value and the convergence parameter) and the inverse uncertainty propagation using Bayesian inference (such as the pre-assigned tolerance, prior estimate, and standard deviation) in terms of their accuracy and adequacy.     

空气焓值法测量全年能源消耗效率不确定度评定方法研究

以焓值法的直接测试量表征的制冷量和制热量计算公式作为基础数学模型,采用GUM法和蒙特卡洛法相结合的方法来评定全年能源消耗效率(APF)的不确定度。由于焓值法数学模型呈现出明显的非线性,首先使用蒙特卡洛法来验证额定制冷量和额定制热量GUM法评定结果, 验证结果显示2种方法偏差不超过2‰。然后,给出了5个工况下换热量的不确定度评定结果,以此作为APF蒙特卡洛模拟的输入量,并给出了自适应蒙特卡洛法评定APF不确定度模拟流程,得到某空调的APF扩展不确定度评定结果为0.09 kW·h/(kW·h)（k=2）。