Abstract
The paper presents examples of the evaluation of uncertainty components in accordance with the current and revised Guide to the expression of uncertainty in measurement (GUM). In accordance with the proposed revision of the GUM a Bayesian approach was conducted for both type A and type B evaluations.The law of propagation of uncertainty (LPU) and the law of propagation of distribution applied through the Monte Carlo method, (MCM) were used to evaluate associated standard uncertainties, expanded uncertainties and coverage intervals. Furthermore, the influence of the non-Gaussian dominant input quantity and asymmetric distribution of the output quantity y on the evaluation of measurement uncertainty was analyzed. In the case when the probabilistically coverage interval is not symmetric, the coverage interval for the probability P is estimated from the experimental probability density function using the Monte Carlo method. Key highlights of the proposed revision of the GUM were analyzed through a set of examples.
Highlights
Home Search Collections Journals About Contact us My IOPscienceExamples of measurement uncertainty evaluations in accordance with the revised GUM
The Guide to the expression of uncertainty in measurement (GUM) was last amended in 2008
In accordance with the proposed revision of the GUM a Bayesian approach was conducted for both type A and type B evaluations.The law of propagation of uncertainty (LPU) and the law of propagation of distribution applied through the Monte Carlo method, (MCM) were used to evaluate associated standard uncertainties, expanded uncertainties and coverage intervals
Summary
Examples of measurement uncertainty evaluations in accordance with the revised GUM. This content has been downloaded from IOPscience. Ser. 772 012008 (http://iopscience.iop.org/1742-6596/772/1/012008) View the table of contents for this issue, or go to the journal homepage for more. You may be interested in: Special issue on Statistical and Probabilistic Methods for Metrology Walter Bich and Maurice G Cox The grey relational approach for evaluating measurement uncertainty with poor information Zai Luo, Yanqing Wang, Weihu Zhou et al On a Monte Carlo method for measurement uncertainty evaluation and its implementation P M Harris and M G Cox Implementing measurement uncertainty for analytical chemistry: the Eurachem Guide for measurement uncertainty Stephen L R Ellison Metas.UncLib—a measurement uncertainty calculator for advanced problems M Zeier, J Hoffmann and M Wollensack Reference data sets for testing metrology software G J P Kok, P M Harris, I M Smith et al The GUM revision: the Bayesian view toward the expression of measurement uncertainty I Lira Measurement uncertainty and traceability Maurice G Cox and Peter M Harris. B Runje, A Horvatic, V Alar, S Medic and A Bosnjakovic3 1University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Ivana Lucica 1, 10000 Zagreb, Croatia 2Karlovac University of Applied Science, Trg J.J.Strossmayera 9, 47000 Karlovac, Croatia 3Institute of Metrology of Bosnia and Herzegovina, Augusta Brauna 2, 71000 Sarajevo,Bosnia and Herzegovina
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
