Abstract
The analysis shows that many parameters of industrial facilities are determined by computational operations on the results of direct measurements. Such measurements can be arranged according to parallel and parallel-serial structure of the measurement system. Considering that there are interference in the experimental study of industrial facilities, preference is given to a parallel-serial measurement structure, which provides high noise immunity, and also uses only one measuring channel (MC). The bias of the real characteristic of the measuring conversion during the direct measurement of the input quantities, which can take one of the possible values in the given dynamic range, remains constant. In this case, a stochastic relationship arises between the input quantities; hereinafter, this relationship will be called instrumental covariance. The influence of instrumental covariance on the estimated uncertainty of tested parameter depends on arithmetic operations performed on functionally related results of direct measurements. Many such parameters are determined using multiplication and division operations. A simple example is determining the power and resistances from direct current and voltage measurements. The influence of the ratio between the input values on the additional component of the estimated uncertainty of the object parameter due to instrumental covariance is analyzed. Recommendations on how to reduce the impact of instrumental covariance are also given.
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References
JCGM 100:2008, GUM 1995 with minor corrections Evaluation of measurement data—Guide to the Expression of Uncertainty in Measurement. Bureau International des Poids et Mesures, France
ISO/IEC Guide 98-3:2008 Uncertainty of measurement—Part 3: Guide to the expression of uncertainty in measurement. International Organization for Standardization, Switzerland
International vocabulary of metrology. Basic and general concepts and associated terms (VIM). – ICGM, 2012, p. 88
Zakharov, I.P.: Estimating measurement uncertainty on the basis of observed and logical correlation. Meas. Tech. 50(8), 808–816 (2007). https://doi.org/10.1007/s11018-007-0154-8
Volodarsky, E., Warsza, Z., Kosheva, L.A., Idźkowski, A.: Transforming the conversion characteristic of a measuring system used for technical control. In: Szewczyk, R., Kaliczyńska, M. (eds.) Recent Advances in Systems, Control and Information Technology, vol. 543, pp. 524–534. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-48923-0_56
Warsza, Z.L., Puchalski, J.: Estimation of vector uncertainties of multivariable indirect instrumental measurement systems on the star circuit example. J. Phys.: Conf. Ser. 1065 (2018). https://doi.org/10.1088/1742-6596/1065/5/052026, XXII World Congress IMEKO 2018 Belfast. IOP Conf. Series
Warsza, Z.L., Puchalski, J.: Method of the estimation of uncertainties in multiparameter measurements of correlated quantities. In: Proceedings of V Workshop: Applied Methods of Statistical Analysis, Statistical Computation and Simulation, AMSA 2019, 18–20 September Novosibirsk NSTU, pp.47–59. ISSN 2313-870X (2019). https://amsa.conf.nstu.ru/amsa2019/
Warsza, Z.L., Puchalski, J.: Estimation of uncertainties in indirect parameter measurements of correlated quantities. In: Proceedings of 12th International Conference on Measurement 2019, pp. 51–57. Slovak Academy of Sciences, Slovakia (2019). ISBN 978-80-972629-2-1
Warsza, Z.L., Puchalski, J.: Estimation of uncertainties in indirect multivariable measurements part 1. Case of correlated quantities. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) Automation 2020: Towards Industry of the Future, vol. 1140, pp. 309–325. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40971-5_29
Warsza, Z.L., Puchalski, J.: Uncertainty bands of the regression line for autocorrelated data of dependent variable Y. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) Automation 2021: Recent Achievements in Automation, Robotics and Measurement Techniques, vol. 1390, pp. 364–386. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-74893-7_33
Volodars'kiy, Є.T., Kosheva, L.O., Dobrolyubova, M.V.: Informatsiyno-vimiryuval’na sistema ta neviznachenist’ (Information-measuring system and uncertainty). Ukrains’kiy metrologichniy zhurnal (Ukrainian Metrological Journal) 3A, 30–34 (2020). WoS 000604400000005
Warsza, Z.L., Puchalski, J.: Method of estimation uncertainties of indirect multivariable measurement including accuracy of processing function as extension of GUM-S2. In: Pavese, F., Forbes A.B., Zhang N.F., Chunovkina A.G. (eds.) Advanced Mathematical and Computational Tools in Metrology and Testing AMCTM XII, Series on Advances in Mathematics for Applied Sciences, vol. 90, pp. 451–463. World Scientific Publishing Company, London 2021
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Volodarskyi, Y., Warsza, Z.L., Kosheva, L., Sautin, A. (2022). Instrumental Covariance and Its Impact on the Uncertainty of Tested Parameters of Industrial Objects. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2022: New Solutions and Technologies for Automation, Robotics and Measurement Techniques. AUTOMATION 2022. Advances in Intelligent Systems and Computing, vol 1427. Springer, Cham. https://doi.org/10.1007/978-3-031-03502-9_36
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