Measurement Uncertainty Evaluation Method Considering Correlation and its Application to Precision Centrifuge

Abstract Measurement uncertainty evaluation based on the Monte Carlo method (MCM) with the assumption that all uncertainty sources are independent is common. For some measure problems, however, the correlation between input quantities is of great importance and even essential. The purpose of this paper is to provide an uncertainty evaluation method based on MCM that can handle correlated cases, especially for measurement in which uncertainty sources are correlated and submit to non-Gaussian distribution. In this method, a linear-nonlinear transformation technique was developed to generate correlated random variables sampling sequences with target prescribed marginal probability distribution and correlation coefficients. Measurement of the arm stretch of a precision centrifuge of 10-6 order was implemented by a high precision approach and associated uncertainty evaluation was carried out using the mentioned method and the method proposed in the Guide to the Expression of Uncertainty in Measurement (GUM). The obtained results were compared and discussed at last.

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Uncertainty Analysis of a Test Bed for Calibrating Voltage Transformers vs. Temperature

Sensors ◽

10.3390/s19204472 ◽

2019 ◽

Vol 19 (20) ◽

pp. 4472 ◽

Cited By ~ 1

Author(s):

Mingotti ◽

Peretto ◽

Tinarelli ◽

Ghaderi

Keyword(s):

Monte Carlo ◽

Monte Carlo Method ◽

Uncertainty Analysis ◽

Random Effects ◽

Evaluation Method ◽

Uncertainty Evaluation ◽

Test Bed ◽

Uncertainty Sources ◽

The Monte Carlo Method

The paper addresses the evaluation of the uncertainty sources of a test bed system for calibrating voltage transformers vs. temperature. In particular, the Monte Carlo method has been applied in order to evaluate the effects of the uncertainty sources in two different conditions: by using the nominal accuracy specifications of the elements which compose the setup, or by exploiting the results of their metrological characterization. In addition, the influence of random effects on the system accuracy has been quantified and evaluated. From the results, it emerges that the choice of the uncertainty evaluation method affects the overall study. As a matter of fact, the use of a metrological characterization or of accuracy specifications provided by the manufacturers provides respectively an accuracy of 0.1 and 0.5 for the overall measurement setup.

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MEASUREMENT UNCERTAINTY EVALUATION OF BRINELL HARDNESS TEST: GUM AND MONTE CARLO METHOD

Periódico Tchê Química ◽

10.52571/ptq.v15.n30.2018.255_periodico30_pgs_252_258.pdf ◽

2018 ◽

Vol 15 (30) ◽

pp. 252-258

Author(s):

L. TREVISAN ◽

D. A. K. FABRICIO

Keyword(s):

Mathematical Method ◽

Monte Carlo ◽

Monte Carlo Method ◽

Measurement Uncertainty ◽

Brinell Hardness ◽

Hardness Test ◽

Mechanical Tests ◽

Uncertainty Evaluation ◽

The Monte Carlo Method ◽

Measurement Uncertainty Evaluation

The Brinell hardness test is one of the most used mechanical tests in the industry to assure the quality of metallurgical processes. Based on the measured values, it is necessary to describe the measurement uncertainty values associated with the mathematical method used. Thus, measurement uncertainty values describe the reliability of the experimental results. The calculation of measurement uncertainty can be performed in several ways, and the method described by ISO/GUM is the most used by ISO/IEC 17025 accredited laboratories. The main objective of this work is to compare measurement uncertainty values based on different sources of uncertainty used in the measurement uncertainty evaluation for two Brazilian laboratories accredited by Cgcre/INMETRO. In addition, uncertainty values obtained by the GUM method and by the Monte Carlo method were compared. The results show that there is no great variation in the measurement uncertainty values as a function of the mathematical method used.

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"GUIDE TO THE EXPRESSION OF UNCERTAINTY IN MEASUREMENT" (GUM) AND "MUTUAL RECOGNITION OF NATIONAL MEASUREMENT STANDARDS AND OF CALIBRATION AND MEASUREMENT CERTIFICATES ISSUED BY NATIONAL METROLOGY INSTITUTES" (MRA): SOME PROBLEMS OF DATA PROCESSING AND MEASUREMENT UNCERTAINTY EVALUATION

Advanced Mathematical and Computational Tools in Metrology V ◽

10.1142/9789812811684_0007 ◽

2001 ◽

Cited By ~ 5

Author(s):

A. CHUNOVKINA ◽

A. CHURSIN

Keyword(s):

Data Processing ◽

Measurement Uncertainty ◽

Mutual Recognition ◽

Uncertainty Evaluation ◽

Uncertainty In Measurement ◽

Measurement Standards ◽

National Metrology Institutes ◽

National Measurement ◽

Measurement Uncertainty Evaluation ◽

Expression Of Uncertainty

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Research on Measurement Uncertainty Evaluation Methods Based on Bayesian Principle

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.381-382.583 ◽

2008 ◽

Vol 381-382 ◽

pp. 583-586

Author(s):

Xiao Huai Chen ◽

Z.Y. Cheng ◽

Ye Tai Fei

Keyword(s):

Measurement Uncertainty ◽

Evaluation Method ◽

Dynamic Measurement ◽

Uncertainty Evaluation ◽

Small Samples ◽

Current Application ◽

Dynamic Data ◽

Dynamic Uncertainty ◽

Method Of Measurement ◽

Measurement Uncertainty Evaluation

In current application of measurement uncertainty evaluation, dynamic uncertainty evaluation simply uses the static uncertainty methods. To change the situation, a new evaluation method of measurement uncertainty is investigated based on Bayesian principle in this paper. Bayesian evaluation method uses conjugate normal-inverted gamma distribution as the distribution function in uncertainty evaluation, which can be employed to evaluate both static and dynamic measurement uncertainty. The evaluation method put forward in this paper can achieve higher evaluation accuracy than the conventional methods, particularly in processing dynamic data with small samples. It has been proved in theory and by computer simulation.

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