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Linearity error as component of A/D converter uncertainty

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Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Analog-to-digital (A/D) converters are basic elements of measuring chains since they deliver measurement data being a carrier of digital information in measurement instruments and systems. Taking into account that the quality of the information depends on accuracy of data, one should evaluate the uncertainty of A/D conversion results. One of the uncertainty component is connected with the linearity error of an A/D converter. This error has to be contained in the uncertainty budget, however, it is possible if the error is described in probabilistic categories. The paper presents a way of obtaining such a kind of description, the basis of which is an analysis of A/D conversion as a quantization process consisting in a comparison of the measured quantity with a standard composed of quanta. The linearity error is treated as an effect of random distortion of quanta. A general model of a quantization result, containing both the quantization error and the error caused by quantum distortion, as well as the error generated by thermal noise, has been described. An analysis of correlation coeffcients between these errors has been performed using the Monte Carlo method. A procedure of uncertainty calculation on the basis of the known error distribution has been presented.
Rocznik
Strony
329--343
Opis fizyczny
Bibliogr. 9 poz., rys., tab., wykr.
Twórcy
autor
  • Silesian University of Technology, Institute of Measurement Science, Electronics and Control Poland, jerzy.jakubiec@polsl.pl
Bibliografia
  • 1. Jakubiec J.: “System Oriented Mathematical Model of Single Measurement Result”. Metrology and Measurement Systems. vol. XIII, no. 4, 2006, pp. 405-419.
  • 2. Jakubiec J.: “Measurement Process in Sigma-Delta A/D Converter”. Pomiary Automatyka Kontrola, vol. 54, no. 6, 2008., pp. 280-285. (in Polish).
  • 3. Mac Ghee J., Kulesza W., Henderson I. A., Korczyński M. J.: Measurement Data Handling. Ed. The Technical University of Lodz, Łódź 2001.
  • 4. Papoulis A.: Probability, Random Variables, and Stochastic Processes. McGraw-Hill, Inc., New York 1965.
  • 5. Fueller W. A.: Measurement Error Models. John Wiley & Sons, Inc. 1987.
  • 6. Guide to the Expression of Uncertainty in Measurement. Supplement 1, 2004.
  • 7. Guide to the Expression of Uncertainty in Measurement, ISO/IEC/OIML/BIPM, 1992,1995.
  • 8. Jakubiec J.: “Reductive Interval Arithmetic Application to Uncertainty Calculation of Measurement Result Burdened Correlated Errors”. Metrology and Measurement Systems, vol. X, no. 2, 2003, pp. 137-156.
  • 9. Jakubiec J., Konopka K.: “A Method of Error Source Identification of A/D Measuring Chain”. Proc. 20th IEEE Instrumentation and Measurement Technology Conference IMTC/03, USA, 2003, pp. 1659-1664.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW1-0048-0007
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