COMPARISON OF THE GUM AND MONTE CARLO METHODS FOR THE UNCERTAINTY ESTIMATION IN ELECTROMAGNETIC COMPATIBILITY TESTING

TLDR

Uncertainty estimation in EMC testing is complex; conventional methods assume uncorrelated, symmetric contributions and use linear error propagation, which may compromise reliability. The study compares GUM and Monte Carlo methods for estimating uncertainty in common EMC tests. The authors applied both GUM and Monte Carlo methods to estimate uncertainty for radiated and conducted emissions tests per CISPR 22, IEC 61000‑4‑3, and IEC 1000‑4‑6 standards. The study found no significant difference between GUM and Monte Carlo estimates, though GUM slightly overestimates uncertainty; Monte Carlo offers advantages by avoiding GUM’s assumptions.

Abstract

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 1000-4-6) 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.