Considering of the input quantities distributions in the procedure for measurement uncertainty evaluating on the example of resistance box calibration

Автор(и)

  • Igor Zakharov Kharkiv National University of Radio Electronics, Nauky Ave., 14, 61166, Kharkiv National Scientific Centre “Institute of Metrology”, Myronosytska Str., 42, 61002, Kharkiv, Україна
  • Olesia Botsiura Kharkiv National University of Radio Electronics, Nauky Ave., 14, 61166, Kharkiv, Україна
  • Valerii Semenikhin Kharkiv National University of Radio Electronics, Nauky Ave., 14, 61166, Kharkiv National Scientific Centre “Institute of Metrology”, Myronosytska Str., 42, 61002, Kharkiv, Україна
  • Valeria Fomenko Kharkiv National University of Radio Electronics, Nauky Ave., 14, 61166, Kharkiv, Україна

DOI:

https://doi.org/10.24027/2306-7039.4.2020.224189

Ключові слова:

calibration, resistance box, kurtosis method, expanded uncertainty, law of propagation of expanded uncertainty, uncertainty budget

Анотація

The controversy over estimates of measurement uncertainty in the Guide to the Expression of Uncertainty in Measurement and Supplement 1 to it is considered. It is shown that possible ways to overcome these disagreements are to use the methods developed by the authors. Using the example of resistance calibration on a direct current, the features of taking into account the distribution of input values in the procedure for uncertainty evaluation when using the kurtosis method and low of propagation of expanded uncertainty are shown. A model of direct measurement of the resistance value of a resistance measure using a reference ohmmeter is written, the procedures for measurement uncertainty evaluation are described, and the uncertainty budgets are given. An example of measurement uncertainty evaluation at calibrating a resistance box P33 class 0.2 using a Fluke 8508 A digital multimeter is described. The expanded uncertainty of measurement for this example was estimated based on the NIST Uncertainty Machine web application, which showed good agreement with the estimates obtained by the methods considered.

Посилання

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Uncertainty machine. Available at: https://uncertainty.nist.gov /

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Опубліковано

2020-12-30

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