Evaluation Of Measurement Uncertainty Research Articles

Identification and evaluation of uncertainty contributions to optical current sensor measurement uncertainty

Optical current sensors can replace traditional current sensor due to their several advantages, as electromagnetic immunity, low dimensions, low cost and others. One of the current main challenges is to develop optical current sensors with good accuracy and stability. This paper presents identification and evaluation of uncertainty contributions to measurement uncertainty evaluation of optical current sensors based on Faraday effect.

Evaluation of uncertainty in direct measurement for parameterization of pyrolysis models, Part I: Thermal analysis

A comprehensive uncertainty analysis is an important part of any engineering calculation because it allows stakeholders to assess the confidence in the conclusions that come from the calculation. Without this necessary step in hypothesis testing and model validation, the true accuracy and confidence of predictions cannot be completely quantified. An experimental and analytical approach is presented in this work to determine the kinetics and energetics of pyrolysis which form the foundation for larger scale models of material burning. A detailed uncertainty quantification which utilized the generalized polynomial chaos expansion method is presented to propagate uncertainty through the pyrolysis model representation of a thermal analysis experiment. Using this methodology, the uncertainty in the predictions was found to be comparable to the uncertainty in the experimental thermal analysis data within calibration conditions. Sensitivity analyses revealed the largest contributor to uncertainty in heat flow rate predictions was the specific heat capacity of all components. Comparison of the uncertainty quantification and sensitivity analyses to well-known results validated the method of uncertainty propagation for more complex scenarios.

A suggested protocol for value assignment using isotope-dilution-LC-MS/MS in reference measurement procedures – Exemplary application for ciprofloxacin in serum

BackgroundReference measurement procedures for assigning values to calibration materials play a crucial role in the concept of metrological traceability in laboratory medicine. ISO standard 15,193 specifies the requirements for such measurement procedures, albeit in a very general terms. Materials and methodsA standard structure of analysis series for value assignment by LC-MS/MS was developed and tested,this structure was complemented by a spreadsheet file for result calculation, metadata evaluation, and finally validation and confirmation of individual analysis runs and individual results, and measurement uncertainty evaluation. This framework was applied to a procedure for the quantification of ciprofloxacin in serum as an example. ResultsThe approach of a detailed description of the analytical procedures of isotope dilution LC-MS/MS reference measurement methods together with a highly standardized spreadsheet-based method for data processing was found to be practical and efficient. The described measurement procedure for the quantification of ciprofloxacin in serum was found to be fit for purpose. ConclusionA standardized, detailed procedure for the application of isotope dilution LC-MS/MS in reference measurement procedures can complement the ISO 15193 standard with respect to this particular analytical technique, which is now widely used in the context of metrological traceability in laboratory medicine.

A novel uncertainty evaluation method based on the particle filter and beta distribution for data with unknown distribution.

Uncertainty evaluation for unknown distribution data is a key problem to be solved in uncertainty evaluation theory. To evaluate the measurement uncertainty of data with unknown distributions, a novel uncertainty evaluation method based on the particle filter (PF) and beta distribution is proposed in this paper. A beta distribution with wide adaptability was adopted as the distribution type of measurement results, the parameters of the beta distribution were taken as the parameters to be estimated, and a state-space model was established. The PF method, suitable for non-Gaussian data, was utilized to obtain the estimates of the parameters of the beta distribution according to the measurement results. Finally, the best estimates of the measurement results and their uncertainty were calculated using the beta distribution parameters. Simulation results show that the proposed method is adaptive to accurately evaluate the measurement uncertainties of data, especially for non-Gaussian distribution data or asymmetrically distributed data. Multiple evaluation results show that the method has good robustness. The experimental results for the drift errors of a laser interferometer show that the uncertainty result of the proposed method is consistent with the Monte Carlo method. This method is suitable for a variety of distribution types that can be characterized through beta distribution and can solve the optimal estimation and uncertainty evaluation of most measurement results with unknown distribution types.

Bottom-up and top-down measurement uncertainty evaluation for multivariate spectrophotometric procedures

Multivariate spectrophotometric procedures are widely used in drug analysis to quantify one or more substances in a sample. Even if the analytical procedure is validated, there is always some degree of uncertainty related to the analytical results. No studies were found in the literature that evaluated the measurement uncertainty for multivariate spectrophotometric procedures. Thus, this paper aims to estimate the measurement uncertainty of the quantification of acetylsalicylic acid (ASA) and salicylic acid (SA), in an ASA samples, by a stability-indicating multivariate spectrophotometric procedure. The measurement uncertainties of ASA and SA were estimated by the top-down approach, using the Nordtest/ISO 11352 and VAM Project methodologies, and by the bottom-up approach, using the Monte Carlo method (MCM). The calculation of measurement uncertainty considered uncertainty sources related to the preparation of sample and standard solutions and the impact of these sources on the model obtained by partial least squares (PLS). The uncertainty of the spectra was estimated by bootstrap resampling method. The expanded uncertainties estimated for ASA were 3.7%, 4.1% and 3.7% calculated by MCM, Nordtest/ISO 11352 and VAM Project, respectively. The expanded uncertainties estimated for SA considering the concentration range from 2.67 to 6.67 μg/mL were 0.25, 0.36 and 0.35 μg/mL and for the concentration range from 6.67 to 18.67 μg/mL were 5.3%, 5.3% and 6.0% calculated by MCM, Nordtest/ISO 11352 respectively. It was concluded that the developed methodology allows to estimate the measurement uncertainty of a multivariate spectrophotometric analytical procedure by bottom-up and top-down approaches.

Uncertainty analysis method of internal drag measurement for flow-through model in high-speed wind tunnel

For aircraft with complex air intake and exhaust systems, the flow-through model simulating the internal flow passage is generally used to reduce the interference of internal and external flow fields when carrying out high-speed wind tunnel tests. The drag generated by the internal flow passage needs to be accurately measured and deducted from the full-model aerodynamic force, so as to obtain the aerodynamic characteristics of the aircraft to be tested. However, the existing internal drag measurement methods do not evaluate the uncertainty of the measurement results, which leads to the failure to evaluate the reliability of the flow-through model high-speed wind tunnel test results. This paper introduces the device and measurement method of internal drag of flow-through model. Based on GUM uncertainty analysis method, the analysis method of internal drag measurement uncertainty is established through strict mathematical derivation, which provides technical support for the evaluation of internal drag measurement uncertainty.

Measuring the indication error of the Fizeau interferometer and evaluating its uncertainty

The indication error, as a characteristic of the interferometer, can be obtained through verification and calibration, and the uncertainty during measurement represents the reliability of the indication error. This paper employed the modified absolute measurement method to gain the indication error. First, the improved absolute algorithm was derived. Then, the absolute measurements were conducted with a 150-mm-aperture Fizeau interferometer. The measured indication error conforms to the maximum allowable error of the interferometer. Finally, the measurement uncertainty evaluation was carried out based on the method to characterize the indication error’s dependability, which was much less than one-third of the maximum allowable error.

Dynamic modeling and performance evaluation of piezoelectric impact drive system based on neural network

In metrology and industrial design, the evaluation of measurement uncertainty and error is crucial to the measurement process. The Guide to the Expression of Uncertainty in Measurement and its supplementary documents have established a unified framework and standard for evaluating measurement uncertainty. However, a reasonable method for evaluating dynamic measurement uncertainty has not yet been proposed. By analyzing the dynamic measurement system, and using the long short-term memory time neural network to model the nonlinear dynamics represented by a piezoelectric drive platform, this paper evaluates the system’s dynamic measurement uncertainty through deep integration methods. Bayesian theory is used to propagate probability densities, and experimental results demonstrate the effectiveness of this method for assessing dynamic measurement uncertainty.

Evaluation of Measurement Uncertainty for Fourth Axis Error Calibration of Coordinate Measuring Machines

According to the calibration method defined in the specification, a measurement uncertainty evaluation method for the fourth axis error calibration results of coordinate measuring machines is proposed. Establish a measurement uncertainty evaluation model based on calibration methods and analyze the sources of each uncertainty. Based on measurement examples, the evaluation process of measurement uncertainty for the fourth axis error calibration of coordinate measuring machines is elaborated.

Evaluation and Expression of Measurement Uncertainty of Axis Positioning Accuracy of Machine Tools

Measure the axis bidirectional positioning accuracy of machine tools with guide rails of 2m, 4m and 10m in length, establish an evaluation model of Measurement uncertainty, and analyze the sources of each uncertainty. Combined with the measurement example, the evaluation process of Measurement uncertainty of axis positioning accuracy of machine tools is expounded, and the linear expression formula of Measurement uncertainty is given.

Evaluation of Measurement Uncertainty Obtained with a Tool Probe on a CNC Machine Tool

Evaluation of Measurement Uncertainty Obtained with a Tool Probe on a CNC Machine Tool

Performance Evaluation of Uncertainty in Measurement for Dynamic Young’s Modules of Elasticity

Performance Evaluation of Uncertainty in Measurement for Dynamic Young’s Modules of Elasticity

Features of measurement uncertainty evaluation during calibration of digital ohmmeters

The scheme for transferring the size of the unit of resistance during the calibration of digital ohmmeters at direct current is considered. The procedure for the measurement uncertainty evaluation is described: recording the measurement model and its refinement, evaluation of input and measured values, evaluation of standard uncertainties of the input and measured values, evaluation of the expanded uncertainty. The refined model includes the dependence of the resistance of the reference resistor on temperature and a correction to the drift of the resistance value of the reference resistor since its last calibration. To evaluate the expanded uncertainty, the kurtosis method was used. An uncertainty budget has been made, including the kurtosis of input and measured values. The use of the Excel package makes it possible to implement, based on this budget, a program for automation of measurement uncertainty calculations. An example of the measurement uncertainty evaluation during the calibration of a digital ohmmeter of type 2318 at a point of 1 mOhm using an electrical resistance coil R310 with an accuracy class of 0.01 is considered. The influence of nonlinearity of the measurement model on the estimates of the numerical value of the measurand and its combined standard uncertainty is studied. To verify the results, the distribution law of the measurand was modelled by the Monte Carlo method. An algorithm for determining the expanded uncertainty using the NIST Uncertainty Machine web application for the missing confidence level of 0.9545 is proposed. The comparison of the results of the measurement uncertainty evaluation by the kurtosis and Monte Carlo methods has shown their good agreement.

Measurement science meets the reproducibility challenge

Measurement science is particularly well equipped not only to meet reproducibility challenges arising within the field of metrology, but also to suggest strategies and best practices for how such challenges can be met in other fields. This contribution illustrates three such challenges, in three different fields, and proposes ways to address them that can supplement the only way in which reproducibility challenges in science can be resolved definitively: by validated scientific advances that point toward the truth. The first example concerns a large interlaboratory, international comparison of the measurement of the mass fraction of silica in a granite reference material, using classical methods of wet analytical chemistry, carried out in the 1940s. The results delivered a shock to analysts worldwide about the state of the art at the time. The challenge was magnified by the fact that none of the measured values was qualified with an evaluation of measurement uncertainty. We offer an approach developed by Andrew Rukhin from NIST, for how to compute a meaningful consensus value in such case, and explain how the associated uncertainty can be characterized. The second example is about the currently hot topic of the Hubble tension, which refers to the mutual inconsistency of the measurement results, obtained by different methods, for the Hubble-Lemaître constant, which expresses the rate of expansion of the Universe. We suggest that such tension can be quantified in terms of the dark uncertainty that figures as a parameter in a laboratory random effects model, thus providing an objective metric whereby progress toward resolving such tension can be gauged. The third example discusses two sources of lack of reproducibility: on the one hand, the fact that different laboratories produced strikingly discrepant values for the mass fraction of arsenic in kudzu; on the other hand, that different models can be fitted to these data, each producing its own set of results. Here we use a Bayesian model selection criterion to choose one from among four models that are natural candidates to address this double reproducibility challenge. This third example also affords us the opportunity to deflate two widespread myths: that one needs at least four observations to obtain a Bayesian evaluation of standard uncertainty, and that sample standard deviations of small samples are systematically too small.

Uncertainty Evaluation and Analysis of Enterobacteriaceae Count in Proficiency Testing

Objective: To analyze and evaluate the uncertainty the results of the proficiency testing (ACAS-PT 1083 (2021)) of enterobacteriaceae count. Methods: According to the operational instructions of capability verification and GB 4789.41-2016 National food safety standard-Food microbiology test- Enterobacteriaceae count, the enterobacteriaceae of the capability verification sample were tested quantitatively. The suspected colonies were identified by VITEK2. The uncertainty of test results was assessed in accordance with JJF 1059.1-2012 and GB/T 27418-2017 Evaluation and expression of measurement uncertainty. Results: The test result of sample 21-Q997 was 4.2 ×10³ CFU/mL. The test results were satisfactory as the Z values of the proficiency test was -0.8 (|Z| was less than 2.0). The extended uncertainty of enterobacteriaceae test results was 0.0288 at 95% confidence interval. The VITEK 2 identification system identified suspicious colonies as E. coli with 99% confidence. Conclusion: The evaluation results demonstrated that the laboratory had the ability to detect the count of enterobacteriaceae in food and the accuracy of VITEK2's auxiliary identification of suspicious colonies has been verified. The evaluation method established in this study can evaluate the uncertainty of the quantitative detection of enterobacteriaceae count with ability verification, and is suitable for the evaluation of the relevant uncertainty in laboratories with similar testing conditions.

Uncertainty evaluation of photoluminescence quantum yield measurement in an integrating hemisphere-based instrument.

We present an integrating hemisphere-based (i.e.,a variant of integrating spheres) implementation of the indirect illumination method for absolute photoluminescence quantum yield measurements, which is a recommended method in the international standard IEC 62607-3-1:2014. We rigorously formulated a mathematical model and a measurement procedure for the absolute photoluminescence quantum yield measurement in the integrating hemisphere-based system. The measurement system was calibrated using an Hg-Ar discharge lamp and spectral irradiance standard lamps for wavelength and relative spectral radiant flux scales, respectively. Furthermore, we identified and evaluated uncertainty components involved in the photoluminescence quantum yield (PLQY) measurement. To validate our measurement system, we applied it to the two de facto standard dyes: quinine bisulfate (QBS) and fluorescein (FLS). Consequently, their PLQY values were determined to be 0.563±0.024 (k=2) and 0.876±0.032 (k=2) for, respectively, QBS and FLS, which are consistent with previous reports.

Evaluation of Uncertainty in Determination of Soluble Content of Plastic Body Modified Asphalt Waterproof Coil

Plastic body modified asphalt waterproof rolling material should be tested for soluble substance content in accordance with the provisions of 5.3 Material performance clause in GB 18243-2008 Plastic Body Modified Asphalt Waterproof Rolling Material, and the corresponding minimum soluble substance content requirements are given according to the type and thickness of the rolling material. Soluble substance content detection shall be conducted according to the method specified in GB/T 328.26-2007 Test Method for Building Waterproof Roll Part 26 Soluble Substance Content of Asphalt Waterproof Roll (impregnated Material Content). In this assessment, the soluble matter content of APP I PY M PE 3 10 products produced by an enterprise was determined according to the corresponding standards, and the measurement uncertainty of this test was assessed according to the test process and results. The results were calculated as follows: The relative standard uncertainty of soluble content of the plastic body modified asphalt waterproof coil is μ= 34.42g/m2, and the extended uncertainty is U=(2482 ± 69) g/m2. The data is taken as an integer according to the standard, so the extended part certainty is 69g/m2. It can be seen from the process of evaluating measurement uncertainty that its main source is the introduction of weighing mass before and after specimen individual differences.

Mathmet Measurement Uncertainty Training activity – Overview of courses, software, and classroom examples

A collaborative activity on “Measurement Uncertainty (MU) Training” under the auspices of the European Metrology Network for Mathematics and Statistics (Mathmet) is underway. This abstract reports on the progress to undertake surveys of existing training courses on MU, software for MU evaluation, and classroom examples to support the understanding of methods for MU evaluation. An appreciable number of training courses, software and examples have been identified and are currently under review. These tools and materials will be analysed and categorised according to their main features and characteristics. Special attention will be given to their adherence to the JCGM guidelines (i.e., JCGM 100:2008, JCGM 101:2008 and JCGM 102:2011). It is hoped that the knowledge assembled in this activity will help practitioners to make good choices about appropriate material to support their training needs, as well as help developers of training material to ensure good coverage of their training products and target them at user needs.

Probability theory as a logic for modelling the measurement process

The problem of the nature of probability has been drawn to the attention of the measurement community in the comparison between the frequentist and the Bayesian views, in the expression and the evaluation of measurement uncertainty. In this regard, it is here suggested that probability can be interpreted as a logic for developing models of measurement capable of accounting for uncertainty. This contributes to regard measurement theory as an autonomous discipline, rather than a mere application field for statistics. Following a previous work in this line of research, where only measurement representations, through the various kinds of scales, were considered, here the modelling of the measurement process is discussed and the validity of the approach is confirmed, which suggests that the vision of probability as a logic could be adopted for the entire measurement theory. With this approach, a deterministic model can be turned into probabilistic by simply shifting from a deterministic to a probabilistic semantic.

Implementation of Monte Carlo Simulation in Evaluation of The Uncertainty of Rainfall Measurement

Many factors trigger the uncertainty of rainfall measurement. Several factors can be related to the instruments, weather conditions, and acquisition methods. The degree of uncertainty could be obtained through the calibration process. In principle, rain gauges are calibrated based on the standard process ruled by ISO/IEC 17025 using the law of propagation of uncertainty (LPU). However, LPU requires complex and complicated mathematical calculations. An alternative approach is needed to evaluate measurement uncertainty besides the LPU method. This research used the Monte Carlo method to determine the uncertainty during the rainfall measurement. This method involves repeated random simulations by providing probability distribution on the input and output of rainfall measurement. The results showed that the Monte Carlo method can accurately determine the uncertainty of rainfall measurement. In addition, the uncertainty analysis also showed that instrument inaccuracy is the most significant factor that causes the uncertainty of rainfall measurement.