Processing Dependent Systematic Contributions to Measurement Uncertainty

In the measurement field, the correlation of two uncertainty contributions is a form of probabilistic association that can significantly affect the final uncertainty associated to the measurement result. The Guide to the expression of uncertainty in measurement recommends a mathematical approach to deal with correlated random contributions to measurement uncertainty. A similar kind of association, or dependence, can characterize also different systematic contributions to uncertainty and should be taken into account when evaluating their effect on the final measurement uncertainty. This paper discusses a new approach to handle such systematic contributions when they are represented by symmetric possibility distributions (PDs) of the same shape. This method allows one to build the joint PD of two systematic contributions, both dependent and independent, and propagate them through a generic measurement function.

[1]  Didier Dubois,et al.  Probability-Possibility Transformations, Triangular Fuzzy Sets, and Probabilistic Inequalities , 2004, Reliab. Comput..

[2]  Alessandro Ferrero,et al.  Uncertainty: Only One Mathematical Approach to Its Evaluation and Expression? , 2012, IEEE Transactions on Instrumentation and Measurement.

[3]  Craig B. Borkowf,et al.  Random Number Generation and Monte Carlo Methods , 2000, Technometrics.

[4]  A. Ferrero,et al.  The random-fuzzy variables: A new approach for the expression of uncertainty in measurement , 2003, Proceedings of the 20th IEEE Instrumentation Technology Conference (Cat. No.03CH37412).

[5]  Gilles Mauris,et al.  Representing and Approximating Symmetric and Asymmetric Probability Coverage Intervals by Possibility Distributions , 2009, IEEE Transactions on Instrumentation and Measurement.

[6]  Alessandro Ferrero,et al.  A comparative analysis of the statistical and random-fuzzy approaches in the expression of uncertainty in measurement , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).

[7]  A. Ferrero,et al.  The use of random-fuzzy variables for the implementation of decision rules in the presence of measurement uncertainty , 2005, IEEE Transactions on Instrumentation and Measurement.

[8]  Alessandro Ferrero,et al.  Modeling and Processing Measurement Uncertainty Within the Theory of Evidence: Mathematics of Random–Fuzzy Variables , 2007, IEEE Transactions on Instrumentation and Measurement.

[9]  Simona Salicone,et al.  Measurement Uncertainty: An Approach Via the Mathematical Theory of Evidence , 2006 .

[10]  Alessandro Ferrero,et al.  The theory of evidence for the expression on uncertainty in measurement , 2005 .

[11]  Alessandro Ferrero,et al.  The construction of random-fuzzy variables from the available relevant metrological information , 2008, IEEE Transactions on Instrumentation and Measurement.

[12]  G. Mauris,et al.  Possibility Expression of Measurement Uncertainty In a Very Limited Knowledge Context , 2006, Proceedings of the 2006 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement (AMUEM 2006).

[13]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[14]  Alessandro Ferrero,et al.  An Original Fuzzy Method for the Comparison of Measurement Results Represented as Random-Fuzzy Variables , 2007, IEEE Transactions on Instrumentation and Measurement.

[15]  L. Zadeh Fuzzy sets as a basis for a theory of possibility , 1999 .