Application Of Bias Randomization In Evaluation Of Measuring Instrument Capability

• Autorzy: Fotowicz P.

Identyfikatory

DOI

10.1515/mms-2015-0041

• Języki publikacji: EN

Abstrakty

EN

The paper deals with the problem of bias randomization in evaluation of the measuring instrument capability. The bias plays a significant role in assessment of the measuring instrument quality. Because the measurement uncertainty is a comfortable parameter for evaluation in metrology, the bias may be treated as a component of the uncertainty associated with the measuring instrument. The basic method for calculation of the uncertainty in modern metrology is propagation of distributions. Any component of the uncertainty budget should be expressed as a distribution. Usually, in the case of a systematic effect being a bias, the rectangular distribution is assumed. In the paper an alternative randomization method using the Flatten-Gaussian distribution is proposed.

Słowa kluczowe

EN

measurement uncertainty; bias randomization; measuring instrument capability

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2015
• Tom: Vol. 22, nr 4
• Strony: 513--520
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr., wzory

Twórcy

autor

Fotowicz P.

• Central Office of Measures, Elektoralna 2, 00-139 Warszawa, Poland

Bibliografia

• [1] Fotowicz, P. (2010). Systematic effect as a part of the coverage interval. Metrol. Meas. Syst., 17, 439-446.
• [2] Fotowicz, P. (2014). Methods for calculating the coverage interval based on the Flatten-Gaussian distribution. Measurement, 55, 272-275.
• [3] Guide to the Expression of Uncertainty in Measurement. ISO 1995.
• [4] International vocabulary of metrology - Basic and general concepts and associated terms (VIM). JCGM 200:2012.
• [5] Statistical methods in process management - Capability and performance - Part 7: Capability of measurement processes. ISO 22514-7:2012.
• [6] Evaluation of measurement data - Supplement 1 to the Guide - Propagation of distribution using a Monte Carlo method. JCGM 101:2008.
• [7] Pendrill, L.R. (2006). Optimised measurement uncertainty and decision-making when sampling by variables or by attribute. Measurement, 39, 829-840.
• [8] Ardimento, G., Clemente, E. (2008). Some simple considerations about the Test-Uncertainty Ratio (TUR) in legal metrology. OIML Bulletin, XLIX(3-4), 5-10.
• [9] Kallgren, H., Lauwaars, M., Magnusson, B., Pendrill, L., Taylor, P. (2003). Role of measurement uncertainty in conformity assessment in legal metrology and trade. Accreditation and Quality Assurance, 8(12), 541-547.
• [10] Morinaka, H. (2006). Uncertainty in type approval and verification. OIML Bulletin, XLVII(1), 5-11.
• [11] Sommer, K.D., Kochsiek, M. (2002). Role of measurement uncertainty in deciding conformance in legal metrology. OIML Bulletin, XLIII(2), 19-24.
• [12] Grinten, J., Spek, A. (2014). Conformity assessment using Monte Carlo methods. OIML Bulletin, LV(1), 5-12.