拉伸试验能力验证结果的稳健统计指定值及其不确定度

利用金属材料室温拉伸试验能力验证结果的中位值作为初始稳健平均值,用其稳健标准偏差对其进行修正得到修正后的稳健平均值及稳健标准偏差,以此方法反复修正直至稳定,得到能力验证结果的指定值,最后再根据稳定后的稳健标准偏差计算指定值的不确定度。结果表明:对该次拉伸试验结果进行二次修正后,其稳健平均值及稳健标准偏差均已稳定,分别为799和6.23 N·mm~(-2);因此该次金属材料室温拉伸试验能力验证结果的指定值为799 N·mm~(-2);该指定值的标准不确定度为0.7,N·mm~(-2)。     

稳健统计方法在实验室能力验证中的应用

统计方法稳健性的重要意义已为广大专家学者认同,稳健统计学作为数理统计的一个分支,在质量管理工作中发挥着重要作用.现今,统计方法的稳健性问题已得到专家学者的重视,在经典统计法的应用条件不能严格满足时,稳健统计方法的优势便展现出来.实验室能力验证作为质量管理工作的重要组成部分,也得到其强力支持.常用的稳健统计方法有四分位法...     

稳健统计技术在校准实验室能力验证中的应用

为确保CNAS对实验室校准项目认可的有效性,实验室除进行纵向溯源外,还应在认可周期内进行横向能力验证活动。文章主要介绍校准实验室的能力验证判别方法,尤其介绍以稳健统计量Z比分数为判据的稳健统计技术在能力验证活动中的应用。     

声级计校准能力验证与测量不确定度评定

声级计作为噪声测量的主要仪器,保证声级计校准工作的质量是至关重要的。本文介绍声级计A频率计权校准能力验证与测量不确定度分析。     

实验室间比对的能力验证及稳健统计技术第一讲能力验证的基本概念及活动的组织和设计

介绍了能力验证的基本概念。就能力验证的定义、分类、组织和设计、运作及能力评价等问题进行了讨论。介绍了与之相关的稳健统计技术及应用实例。     

实验室间比对的能力验证及稳健统计技术第三讲 能力验证提供者的能力要求

能力验证是判断和监控实验室能力的有效手段，是实验室通过外部措施补充内部质量控制方法的技术，是实验室认可评审技术的重要补充，是维持认可机构间国际互认的基础之一。可见，能力验证活动十分重要。负责协调和提供能力验证计划的组织，即能力验证计划提供者应能确保每一个计划都有能力去胜任地运作。这就要求能力验证提供者或合作者(承担能力验证计划提供者的分包活动的机     

实验室间比对的能力验证及稳健统计技术第二讲能力验证计划的运作和能力评价

上一讲介绍了能力验证的基本概念、定义、分类、组织和设计。本讲主要介绍能力验证计划的运作、能力评价要点、保密和道德等问题。     

导体直流电阻能力验证不确定度分析研究

本实验室参加了由上海国揽检测股份有限公司组织的导体直流电阻能力验证试验,使用FLUKE 5500A精密多功能校准器、FLUKE 8846A精密数字多用表、TES-1360A数显式温湿度计、钢直尺等测量工具对样品进行试验,并对试验过程中产生的不确定度进行分析计算,最后给出不确定度报告的结果表达式。     

分析实验室能力验证数据统计评价方法研究

按照化学分析实验室能力验证国际协调方案的要求 ,对现行统计评价能力验证数据的各方法进行了研究。并提出了一种新的统计方法。组织进行了对金属硅有证标准物质中A1、Ca、Fe含量测定的能力验证活动 ,验证了方法的可靠性。     

实验室间比对的能力验证及稳健统计技术 第六讲 稳健统计技术(二)

在能力验证的稳健统计分析技术中，常将各参加实验室(一律用代码表之)的实验结果、能力统计量Z比分数(包括Z，ZB和Zw)、总计统计量(结果总数N、中位值XM、标准化四分位距Norm IQR、稳健变异系数Cv、极小值Xmin。极大值Xmax和变动范围Range等)归纳并制成各种表格，以便对所有参加实验室的检测能力和能力验证的总体情况进行统计分析和评价。     

薄板拉伸能力验证计划与技术分析

介绍了由宝钢股份公司组织的"BSTC T002金属材料拉伸试验"能力验证计划,以及围绕金属薄板所开展的"两步法"性能均匀性检验.基于稳健统计技术与Z比分数,对15家实验室获得的数据进行了技术分析,讨论了影响试验结果可靠性的测量因素.稳健化置信椭圆揭示出造成PT计划离群结果的主要因素应为系统误差(实验室间变异).     

稳健统计与格拉布斯准则在能力验证结果分析中的应用

分别运用稳健统计（四分位和迭代）法、格拉布斯准则剔除离群值后用经典统计法对热轧带肋钢筋的6组数据进行了统计分析。结果表明:数据组中Z比分值大于5的数据,为离群值。数据组总体服从正态分布或接近正态分布的,3种统计方法结果基本吻合;不存在离群数据的情况下,建议采用稳健统计（四分位或迭代）法进行统计;存在离群数据的情况下,应采用格拉布斯准则剔除离群值后用经典统计法进行统计。对于数据明显偏离正态分布的,不存在离群数据的情况下,建议采用迭代稳健统计法进行统计;存在离群数据的情况下,应采用格拉布斯准则剔除离群值后用经典统计法进行统计。     

我国生活饮用水中铅、砷检测能力验证分析

分析2012年由中国测试技术研究院承担的国家认监委能力验证项目＂CNCA-12-A08生活饮用水中重金属及无机阴离子的检测＂中铅、砷的能力验证结果,对生活饮用水中铅、砷检测方法进行比较和探讨。能力验证结果表明：ICP-MS法测定铅和砷的结果满意率最高,测定结果中位值及平均值与理论值最为接近。     

Robust material requirement planning with cumulative demand under uncertainty

In this paper, we deal with the problem of tactical capacitated production planning with the demand under uncertainty modelled by closed intervals. We propose a single-item with backordering model under small uncertainty in the cumulative demand for the Master Production Scheduling (MPS) problem with different rules, namely the Lot For Lot rule and the Periodic Order Quantity rule. Then we study a general multilevel, multi-item, multi-resource model with backordering and the external demand on components for the Material Requirement Planning (MRP) problem under uncertainty in the cumulative demand. In order to choose robust production plans for the above problems that hedge against uncertainty, we adopt the well-known minmax criterion. We propose polynomial methods for evaluating the impact of uncertainty on a given production plan in terms of its cost and for computing optimal robust production plans for both problems (MPS/MRP) under the assumed interval uncertainty representation. We show in this way that the robust problems (MPS/MRP) under this uncertainty representation are not much computationally harder than their deterministic counterparts.     

Robust topology optimization under load position uncertainty

The topology optimization problem of a continuum structure on the compliance minimization objective is investigated under consideration of the external load uncertainty in its application position with a nonprobabilistic approach. The load position is defined as the uncertain-but-bounded parameter and is represented by an interval variable with a nominal application point. The structural compliance due to the load position deviation is formulated with the quadratic Taylor series expansion. As a result, the objective gradient information to the topological variables can be evaluated efficiently in a quadratic expression. Based on the maximum design sensitivity value, which corresponds to the most sensitive compliance to the uncertain loading position, a single-level optimization approach is suggested by using a popular gradient-based optimality criteria method. The proposed optimization scheme is performed to gain the robust topology optimizations of three benchmark examples, and the final configuration designs are compared comprehensively with the conventional topology optimizations under the loading point fixation. It can be observed that the present method can provide remarkably different material layouts with auxiliary components to accommodate the load position disturbances. The numerical results of the representative examples also show that the structural performances of the robust topology optimizations appear less sensitive to the load position perturbations than the traditional designs.     

Possible Advantages of a Robust Evaluation of Comparisons

Mean values, traditionally used as a location parameter in the analysis of inter-comparisons, are known to lack stability against the effect of “outliers”. It is therefore proposed to replace (or complement) them by the use of medians, which have better statistical “robustness”. An estimate for the corresponding uncertainty is derived and the procedure is illustrated by a numerical example. The simplicity of the suggested robust approach should favor its practical use in a number of metrological applications.     

Robust optimization of structures subjected to stochastic earthquake with limited information on system parameter uncertainty

The optimization of structures subjected to stochastic earthquake and characterized by uncertain parameters is usually posed in the form of non-linear programming with stochastic performance measures where the uncertain parameters are modelled as random variables. Such an approach, however, cannot be adopted in many real life situations when the limited information about uncertainty can be only modelled as of the uncertain but bounded (UBB) type. A robust optimization strategy for stochastic dynamic systems characterized by UBB parameters is proposed in the present study in the framework of the response surface method (RSM). In evaluating the stochastic constraints, repeated computations of the dynamic responses are avoided by applying an adaptive RSM based on the moving least squares method. Numerical results are presented to highlight the effectiveness of the proposed procedure. The effect of parameter uncertainty is also studied by comparing the results obtained from the proposed optimization approach with the conventional stochastic optimization results.     

Robust interval linear programming for environmental decision making under uncertainty

In this study, a robust interval linear programming (RILP) method is developed for dealing with uncertainties expressed as intervals with deterministic boundaries. An enhanced two-step method (ETSM) is also advanced to solve the RILP model. The developed RILP improves upon the conventional interval linear programming (ILP) method since it can generate solution intervals within a larger feasible zone. The decision space based on ETSM contains all feasible solutions, such that no useful information is neglected. Moreover, the RILP can guarantee the stability of the optimization model due to no violation for the best-case constraints. The results also suggest that the RILP is effective for practical environmental and engineering problems that involve uncertainties.     

A Bayesian framework for accelerated reliability growth testing with multiple sources of uncertainty

Reliability growth tests are often used for achieving a target reliability for complex systems via multiple test‐fix stages with limited testing resources. Such tests can be sped up via accelerated life testing (ALT) where test units are exposed to harsher‐than‐normal conditions. In this paper, a Bayesian framework is proposed to analyze ALT data in reliability growth. In particular, a complex system with components that have multiple competing failure modes is considered, and the time to failure of each failure mode is assumed to follow a Weibull distribution. We also assume that the accelerated condition has a fixed time scaling effect on each of the failure modes. In addition, a corrective action with fixed ineffectiveness can be performed at the end of each stage to reduce the occurrence of each failure mode. Under the Bayesian framework, a general model is developed to handle uncertainty on all model parameters, and several special cases with some parameters being known are also studied. A simulation study is conducted to assess the performance of the proposed models in estimating the final reliability of the system and to study the effects of unbiased and biased prior knowledge on the system‐level reliability estimates.