The rigorous uncertainty estimation in Electromagnetic Compatibility (EMC) testing is a complex task that has been addressed through a simplified approach that typically assumes that all the contributions are uncorrelated and symmetric, and combine them in a linear or linearized model using the error propagation law. These assumptions may affect the reliability of test results, and therefore, it is advisable to use alternative methods, such as Monte Carlo Method (MCM), for the calculation and validation of measurement uncertainty. This paper presents the results of the estimation of uncertainty for some of the most common EMC tests, such as: the measurement of radiated and conducted emissions according to CISPR 22 and radiated (IEC 61000-4-3) and conducted (IEC 61000-4-6) immunity, using both the conventional techniques of the Guide to the Expression of Uncertainty in Measurement (GUM) and the Monte Carlo Method. The results show no significant differences between the uncertainty estimated using the aforementioned methods, and it was observed that the GUM uncertainty framework slightly overestimates the overall uncertainty for the cases evaluated here. Although the GUM Uncertainty Framework proves to be adequate for the particular EMC tests that were considered, generally the Monte Carlo Method has features that avoid the assumptions and the limitations of the GUM Uncertainty Framework.
2., EMC Measurement Uncertainty: A Handy Guide, Schaffner EMC Systems, 2002.
3., The Expression of Uncertainty in EMC Testing, 1st Ed., UKAS Publication LAB 34, 2002.
4., "Joint Committee for Guides in Metrology," Evaluation of Measurement Data Guide to the Expression of Uncertainty in Measurement.
5. Yeung, H. and C. E. Papadopoulos, "Natural gas energy flow (quality) uncertainty estimation using Monte Carlo simulation method," International Conference on Flow Measurement FLOMEKO, 2000.
6., "Joint Committee for Guides in Metrology," Guide to the Expression of Uncertainty in Measurement Propagation of Distributions Using a Monte Carlo Method, 2008.
7. Koch, K. R., "Evaluation of uncertainties in measurements by Monte-Carlo simulations with an application for laserscanning," Journal of Applied Geodesy, Vol. 2, 67-77, 2008.
8. Jing, H., M.-F. Huang, Y.-R. Zhong, B. Kuang, and X.-Q. Jiang, "Estimation of the measurement uncertainty based on quasi Monte-Carlo method in optical measurement," Proceedings of the International Society for Optical Engineering, 2007.
9. Khu, S. T. and M. G. Werner, "Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling," Hydrology and Earth System Sciences, Vol. 7, No. 5, 680-690, 2003.
10. Andræ, A. S. G., P. Mller, J. Anderson, and J. Liu, "Uncertainty estimation by Monte Carlo simulation applied to life cycle inventory of cordless phones and microscale metallization processes ," IEEE Transactions on Electronics Packaging Manufacturing, Vol. 27, No. 4, 233-245, 2004.
11. Schueller, G. I., "On the treatment of uncertainties in structural mechanics and analysis," Journal Computers and Structures, Vol. 85, No. 5--6, 235-243, 2007.
12. Willink, R., "On using the Monte Carlo method to calculate uncertainty intervals," Metrologia, Vol. 43, L39-L42, 2006.
13., "Comit International Spcial des Perturbations Radiolectriques," Uncertainties, Statistics, and Limit Modeling Uncertainty in EMC Measurements, 2011.
14. Paez, E. J., C. D. Tremola, and M. A. Azpurua, "A proposed method for quantifying uncertainty in RF immunity testing due to eut presence," Progress In Electromagnetics Research B, Vol. 29, 175-190, 2011.
15., "Joint Committee for Guides in Metrology," International Vocabulary of Metrology Basic and General Concepts and Associated Terms (VIM), 2008.
16., "IEC 60050-131," International Electrotechnical Vocabulary --- Part 131: Circuit Theory, 2002.