NDLI: Virtual calibration environment for a-priori estimation of measurement uncertainty

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2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)

Virtual calibration environment for a-priori estimation of measurement uncertainty

Content Provider	IEEE Xplore Digital Library
Author	Gugg, C. Harker, M. O'Leary, P.
Copyright Year	2014
Description	Author affiliation: Dept. Product Eng., Univ. of Leoben, Leoben, Austria (Gugg, C.; Harker, M.; O'Leary, P.)
Abstract	During product engineering of a measuring instrument, the question is which measures are necessary to achieve the highest possible measurement accuracy. In this context, a measuring instrument's target uncertainty is an essential part of its requirement specifications, because it is an indicator for the measurement's overall quality. This paper introduces an algebraic framework to determine the confidence and prediction intervals of a calibration curve; the matrix based framework greatly simplifies the associated proofs and implementation details. The regression analysis for discrete orthogonal polynomials is derived, and new formulae for the confidence and prediction intervals are presented for the first time. The orthogonal basis functions are numerically more stable and yield more accurate results than the traditional polynomial Vandermonde basis; the methods are thereby directly compared. The new virtual environment for measurement and calibration of cyber-physical systems is well suited for establishing the error propagation chain through an entire measurement system, including complicated tasks such as data fusion. As an example, an adaptable virtual lens model for an optical measurement system is established via a reference measurement. If the same hardware setup is used in different systems, the uncertainty can be estimated a-priori to an individual system's calibration, making it suitable for industrial applications. With this model it is possible to determine the number of required calibration nodes for system level calibration in order to achieve a predefined measurement uncertainty. Hence, with this approach, systematic errors can be greatly reduced and the remaining random error is described by a probabilistic model. Verification is performed via numerical experiments using a non-parametric Kolmogorov-Smirnov test and Monte Carlo simulation.
Sponsorship	IEEE Comput. Intell. Soc.
Starting Page	52
Ending Page	57
File Size	935871
Page Count	6
File Format	PDF
e-ISBN	9781479926145
DOI	10.1109/CIVEMSA.2014.6841438
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-05
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Measurement uncertainty Estimation Virtual calibration Discrete unitary polynomials Regression Polynomials Vectors Calibration Uncertainty estimation Lenses
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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