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Публікація Data processing specific features of COOMET supplementary bilateral comparisons(NNC Institute of metrology, 2020) Zakharov, I. P.; Botsiura, O. A.; Zadorozhna, I.The article provides examples of the application of electrical resistance standards in metrological practice. Existing methods for their calibration are analyzed. It is established that calibration using a comparator is the most accurate and common method for calibrating electrical resistance standards. A model for transferring the size of the resistance unit in calibration of electrical resistance standards using a comparator is considered. An expression is given for the evaluation of the value of measurand. The procedure for estimating the expanded measurement uncertainty based on the kurtosis method is described, and the uncertainty budget is drawn up. An example of evaluation of the measurement uncertainty in calibration of the resistance coil P321 using a resistance comparator P3015 is given. The coincidence of the obtained results with those got using the Monte Carlo method is shown.Публікація Examples of the expanded uncertainty evaluation based on the kurtosis method(2022) Zakharov, I. P.; Botsiura, O. A.Examples of the expanded uncertainty evaluation based on the kurtosis methodПублікація Main stages of calibration of measuring instruments(2023) Zakharov, I. P.; Botsiura, O. A.; Zakharov, O. I.; Zadorozhnaya, I. M.; Semenikhin, V. S.; Novoselov, O. A.The main stages of calibration of measuring instruments are described. The stage of preparation for calibration and its main steps are considered: setting a measurement task, choosing a method and equipment, choosing (developing) calibration methods and their verification (validation). The content of the measurement experiment is presented together with the main measurement methods that can be used to calibrate the indications of measuring instruments and material measures. The main steps of experimental data processing, which lead to the estimation of the numerical value and uncertainty evaluation of the measurand being calibrated, are considered. The preparation of calibration results, including the uncertainty budget and calibration certificate, is described. Procedures for assessing the probability of compliance of a calibrated measuring instrument and material measure with the specified metrological characteristics, as well as for validating their calibration methods, are considered.Публікація Measurement uncertainty evaluation at mass calibration(NNC Institute of metrology, 2020) Zakharov, I. P.; Botsiura, O. A.; Patsenko, O.Example 9.3.1.1 of JCGM-S1 “Mass calibration” is analyzed, which describes the comparison in air of reference and calibrated weights having the same nominal mass. JCGM-S1 compares uncertainty evaluation procedures based on the GUM uncertainty framework and the Monte Carlo method. The article uses the procedure developed by the authors and consists in decomposing the measurement model into a Taylor series of the second order taking into account the kurtoses of the distributions of input quantities. To facilitate the calculations, the finite increment method is used. To find expanded uncertainty, the kurtosis method is used. Good agreement between the results obtained by the proposed method and the result obtained by the Monte Carlo method is shown.Публікація Measurement uncertainty evaluation by kurtosis method at calibration of electrical resistance standards using a comparator(NNC Institute of metrology, 2020) Zakharov, I. P.; Botsiura, O. A.; Semenikhin, V.Presented methods for calibrating electrical resistance standards are ana-lyzed. A model for calibration of electrical resistance measure with help of com-parator is considered. The procedure for measurement uncertainty evaluation based on the kurtosis method is described, and the uncertainty budget is com-piled. An example of measurement uncertainty evaluation at calibration of electri-cal resistance standard using a comparator is given. The coincidence of the ob-tained results with the results obtained using the Monte-Carlo method is shown.Публікація Measurement uncertainty evaluation by kurtosis method at micrometer calibration("Софттрейд", 2020) Zakharov, I. P.; Botsiura, O. A.; Tsybina, I.; Zakharov, O.The procedure for measurement uncertainty evaluation at micrometer calibration by the kurtosis method is considered. The measurement model as the deviation of the micrometer readings from the length of the reference gage block is recorded. The measurement model takes into account the corrections for the micrometer resolution to be calibrated, lack of flatness and departure from parallelism of its measuring faces, as well as for the temperature difference between the gage block and the calibrated micrometer. The input values and their standard uncertainties are estimated. The calculation of the combined standard uncertainty and expanded uncertainty is carried out taking into account the kurtosis of the input quantities. The report presents an uncertainty budget, which can serve as a basis for creating a software tool that facilitates calculations. The proposed procedure was validated by the Monte Carlo method, which showed that it is are adequate for an intended use.Публікація Peculiarities of measurement results processing when calibrating hygrometers(2023) Zakharov, I .P.; Banev, K. I.; Nicolova, E. G.; Diakov, D.; Botsiura, O. A.; Zakharov, O. I.Peculiarities of measurement results processing when calibrating hygrometersПублікація Reduction of the measurand estimate bias for nonlinear model equation(Journal of Physics: Conf. Series, 2018) Botsiura, O. A.; Zakharov, I.; Neyezhmakov, P.The biases arising in the calculation of the measurand estimate at nonlinear measurement models are considered. The possibility of reducing these biases by applying the finite increments method is shown.Публікація Revision of GUM: the suggested alhorythm for processing measurment results(2019) Zakharov, I. P.; Neyezhmakov, P. I.; Botsiura, O. A.The main requirements for the revised GUM are considered. The proposed algorithm for processing measurement results is given. Criteria for compliance with the listed requirements are described. Methods for processing the measurement results with a nonlinear model are proposed.Публікація Study of approaches to determining the required number of multiple observations(Ukrainian Metrological Journal, 2022) Zakharov, I. P.; Botsiura, O. A.; Neyezhmakov, P. I.The necessity to determine the minimum number of observations when developing a measurement procedure in accredited test and calibration laboratories is discussed. The methods of evaluating the number of observations when evaluating the expanded measurement uncertainty using the GUM method, the Monte Carlo method, and based on the Law of the expanded uncertainty propagation are considered. In the first case, a nomogram is constructed that allows determining the minimum required number of multiple observations based on the given values of the expanded measurement uncertainty for a probability of 0.9545, the standard deviation of the scattering of the indications of a measuring instrument and the normally propagated standard instrumental uncertainty of type B. In the case of calculating the measurement uncertainty based on the Monte-Carlo method, a normal law and the Student’s law of propagation with given characteristics was modelled, and on its basis, for a probability of 0.95, a diagram to calculate the required number of observations when performing multiple measurements was constructed. The application of the Law of the expanded uncertainty propagation proved to be the most universal for calculating the required number of observations, since it made it possible to obtain approximating expressions for both probabilities and for the normal and uniform laws attributed to the components of type BПублікація Verification of the Indicating Measuring Instruments Taking into Account their Instrumental Measurement Uncertainty(Institute of Measurement Science, 2017) Zakharov, I. P.; Neyezhmakov, P. I.; Botsiura, O. A.The specific features of the measuring instruments verification based on the results of their calibration are considered. It is noted that, in contrast to the verification procedure used in the legal metrology, the verification procedure for calibrated measuring instruments has to take into account the uncertainty of measurements into account. In this regard, a large number of measuring instruments, considered as those that are in compliance after verification in the legal metrology, turns out to be not in compliance after calibration. In this case, it is necessary to evaluate the probability of compliance of indicating measuring instruments. The procedure of compliance probability determination on the basis of the Monte Carlo method is considered. An example of calibration of a Vernier caliper is given.