Studying additional measurement errors from control tools using an integral functional method

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.171445

Keywords:

control tool, additional error, influencing parameter, integral functional, measurement, static characteristics.

Abstract

Our research has established that under industrial conditions the correction to the result of current measurements when an influencing parameter deviates from the rated value is rarely introduced. In a general case, the procedure for determining an additional measurement error implies that the measured values for an influencing parameter are applied to determine the degree of its deviation while a correction to the current measurement result is calculated as the product of this degree by its rated value.

In a general case, a procedure for determining an additional measurement error includes two stages. At the first stage, the measured values for an influencing parameter are used to determine the degree of its deviation from the rated value. At the second stage, correction is calculated as the product of this degree by the rated value for an additional error.

Such a technique to calculate a correction is time consuming and insufficiently precise, as it does not take into consideration the non-linear dependence of the additional error on a change in the influencing parameter, as well as the current value for the output signal of control tool. To determine the actual value for an influencing parameter and the additional measurement error under industrial operation of control tools, an integral functional method has been proposed. The method implies determining the difference of areas under the nominal and actual acreage static characteristics, limited to a range of measurement. The difference of areas is a function of the output signal of a control tool, a measured parameter and a change in the influencing parameter. It has been shown that the proposed method makes it possible to calculate the actual values for a technological parameter based on its measured and influencing parameters only. We have established regularities between the actual value for a measured parameter, the current value for the output signal from a control tool, and the measured value for an influencing parameter. The proposed method is important and valuable in the operation of computer-integrated control systems of technological parameters, as it makes it possible to determine the actual values for a measured parameter based on relevant algorithms without calculating corrections.

Author Biographies

Yosyf Stentsel, Volodymyr Dahl East Ukrainian National University Tsentralnyi ave., 59-a, Severodonetsk, Ukraine, 93400

Doctor of Technical Sciences, Professor, Head of Department

Department of Computer-Integrated Management Systems

Olga Porkuian, Volodymyr Dahl East Ukrainian National University Tsentralnyi ave., 59-a, Severodonetsk, Ukraine, 93400

Doctor of Technical Sciences, Professor

Department of Computer-Integrated Management Systems

Konstiantyn Litvinov, Volodymyr Dahl East Ukrainian National University Tsentralnyi ave., 59-a, Severodonetsk, Ukraine, 93400

PhD, Senior Lecturer

Department of Computer-Integrated Management Systems

Tetiana Sotnikova, Volodymyr Dahl East Ukrainian National University Tsentralnyi ave., 59-a, Severodonetsk, Ukraine, 93400

PhD, Associate Professor

Department of Computer-Integrated Management Systems

References

  1. DSTU 2681-94. Metrolohiya. Terminy ta vyznachennia (1995). Kyiv: Derzhstandart Ukrainy, 66.
  2. Petrychenko, H., Nazarenko, L., Hots, N. (2014). Metodyka vyznachennia temperaturnoi zalezhnosti popravok dlia zmenshennia diyi vplyvnykh faktoriv na rezultaty vymirennia temperatury za infrachervonym vyprominenniam v umovakh vyrobnytstva. Metrolohiya ta prylady, 4 (48), 8–12.
  3. Calibration of Low-Temperature Infrared Thermometers (2009). MSL Technical Guide 22. Available at: https://pdfs.semanticscholar.org/408a/354c752a4124f68369fa671d93f5acfba7fc.pdf
  4. Pistun, Ye., Matiko, F., Roman, V., Stetsenko, A. (2014). Doslidzhennia pokhybky ultrazvukovykh vytratomiriv za umov spotvorenoi struktury potoku na osnovi CFD-modeliuvannia. Metrolohiya ta prylady, 4 (48), 13–23.
  5. Turkowski, M., Szufleński, P. (2013). New criteria for the experimental validation of CFD simulations. Flow Measurement and Instrumentation, 34, 1–10. doi: https://doi.org/10.1016/j.flowmeasinst.2013.07.003
  6. Random Number Generation and Testing. Available at: http://csrc.nist.gov/groups/ST/toolkit/rng/index.html
  7. Kondrashov, S., Opryshkina, M., Matsak, O. (2015). Kontrol metrolohichnoho stanu system z neliniynymy pervynnymy peretvoriuvachamy za dopomohoiu testovykh vplyviv. Metrolohiya ta prylady, 2, 33–41.
  8. Volodarskiy, E., Koshevaya, L., Dobrolyubova, M. (2017). Otsenivanie kachestva mnogoparametricheskogo tekhnologicheskogo protsessa pri korrelyatsii ego pokazateley. Metrolohiya ta prylady, 5, 20–24.
  9. ISOIEC 17025-2005. General requirements for the competence of testing and calibration laboratories (2005). International Organization for Standardization.
  10. Montgomery, D. C. (2009). Introduction to Statistical Quality Control. John Wiley & Sons, 754.

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Published

2019-06-25

How to Cite

Stentsel, Y., Porkuian, O., Litvinov, K., & Sotnikova, T. (2019). Studying additional measurement errors from control tools using an integral functional method. Eastern-European Journal of Enterprise Technologies, 3(5 (99), 36–43. https://doi.org/10.15587/1729-4061.2019.171445

Issue

Section

Applied physics