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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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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