ISSN (print) 1995-2732
ISSN (online) 2412-9003

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DOI: 10.18503/1995-2732-2025-23-1-138-148

Abstract

Statement of the problem (relevance of the work). The issue of assessing the quality of electrical measuring instruments establishes the need to carry out a list of non-trivial operations aimed at assessing the greatest number of parameters characterizing measuring devices. One of the most common methods of analysis in science is the assessment of results in terms of statistics and probability, describing the possibility of a certain list of operating scenarios or states, as well as assessing the quantitative side of mass phenomena in numerical format. Based on the results obtained, it is possible to produce the most accurate qualitative assessment reflecting the correctness of the functioning, feature and degree of suitability of the device or process under study. Objectives. The purpose of the study is to derive a universal dependence of the key parameter of the accuracy of electrical measuring instruments on the quantitative assessment of the probability of correct verification. Methods Applied. The work uses various methods of scientific, practical and technical research with references to regulatory documentation in the fields of metrology and quality of products and processes. The study uses various mathematical models and methods that evaluate the compliance of the measurement results with the normal distribution law. Additionally, a software package and programming language are used to automate calculations and obtain the results of the constructed dependence. Originality. The paper presents a methodology for calculating the probability of correctness of verification of electrical control and measuring instruments based on the results of predicting the change in the standard deviation parameter depending on the accuracy of the measuring device, which allows obtaining probability results with a high degree of reliability. Result. The paper presents a methodology for constructing a universal operational characteristics of verification based on the conducted results of the study of the distribution law of electrical measurements as a random variable, as well as the results of assessing the predicting change in the accuracy and standard deviation of a sample of measurements. The obtained result shows the dynamics of the manifestation of statistical outliers with varying degrees of both probability and measurement value, falsifying further assessments of the state of devices or systems. Practical Relevance. The result of the conducted research is the definition and justification of an additional quality parameter for electrical control and measuring instruments, the use of which will increase the degree of reliability, accuracy and depth of the conducted qualitative assessments of measuring products or measurement results. Based on the presented research, it is possible to formulate a justification for the procedure for changing the potential accuracy of devices or changing the inter-verification interval based on the results of scheduled certification.

Keywords

electrical control and measuring instruments, quality, operational characteristics of verification, accuracy, probability, statistics, distribution law

For citation

Bobryshov A.P., Kuzmenko V.P., Solyony S.V., Kvas E.S. Universal Operational Characteristics of Quality Assessment of Electrical Control and Measuring Instruments. Vestnik Magnitogorskogo Gosudarstvennogo Tekhnicheskogo Universiteta im. G.I. Nosova [Vestnik of Nosov Magnitogorsk State Technical University]. 2025, vol. 23, no. 1, pp. 138-148. https://doi.org/10.18503/1995-2732-2025-23-1-138-148

Aleksey P. Bobryshov – Postgraduate Student, Assistant of the Department of Electromechanics and Robotics, Saint Petersburg State University of Aerospace Instrumentation, Saint Petersburg, Russia. Email: ap.bobryshov@mail.ru. ORCID 0009-0009-6220-8206

Vladimir P. Kuzmenko – PhD (Eng.), Associate Professor of the Department of Electromechanics and Robotics. Saint Petersburg State University of Aerospace Instrumentation, Saint Petersburg, Russia. Email: mr.konnny@gmail.com. ORCID 0000-0002-0270-4875

Sergey V. Solyony – PhD (Eng.), Associate Professor, Head of the Department of Electromechanics and Robotics. Saint Petersburg State University of Aerospace Instrumentation, Saint Petersburg, Russia. Email: ssv555ssv@yandex.ru. ORCID 0000-0002-7919-3890

Evgeny S. Kvas – Senior Lecturer at the Department of Electromechanics and Robotics. Saint Petersburg State University of Aerospace Instrumentation, Saint Petersburg, Russia. Email: kvas66@bk.ru. ORCID 0000-0001-5164-8454

1. Lisyutina A.I. Product quality: concept and characteristics of quality. Izvestiya Tulskogo gosudarstvennogo universiteta. Tekhnicheskie nauki [Bulletin of Tula State University. Technical sciences], 2020;(3):282-285. (In Russ.)

2. State standard GOST 15467-79. Product quality management. Basic concepts. Terms and definitions. Moscow: Standards Publishing House, 2009, 21 p. (In Russ.)

3. Regulatory document RD 50-605-86. Methodological guidelines for the application of standards for statistical acceptance control. Moscow: Standards Publishing House, 1986, 44 p. (In Russ.)

4. MI 187-86. Methodological guidelines. State system for ensuring the uniformity of measurements. Reliability and requirements for verification methods of measuring instruments. Moscow: Standards Publishing House, 1987, 12 p. (In Russ.)

5. Savkova E.N., Lagunov D.V., Naumenko M.V., Borodenok I.M. Metrological aspects of probability distributions of continuous and discrete quantities in electrical measurements. Materialy Respublikanskoy nauchno-prakticheskoy konferencii [Proceedings of republican scientific and practical conference]. Minsk, 2021, pp.145-151. (In Russ.)

6. Grzhibovsky A.M., Ivanov S.V., Gorbatova M.A. Descriptive statistics using statistical software packages STATISTICA and SPSS. Nauka i zdravoohranenie [Science and Healthcare], 2016;(1):7-23.

7. Borovikov V.P. Populyarnoe vvedenie v programmu STATISTICA [Popular introduction to the STATISTICA program]. Moscow: ComputerPress, 1998, 267 p.

8. Svetlichnaya V.B., Matveeva T.A., Zotova S.A., Stetskova V.V. Pearson's criterion: the essence and application of the method in practice. Materialy IX Mezhdunarodnoy studencheskoy nauchnoy konferencii «Studencheskiy nauchniy forum» [Proceedings of the IX International Student Scientific Conference “Student Scientific Forum”]. Moscow, 2017.

9. Orlov A.I. Nonparametric goodness-of-fit tests of Kolmogorov, Smirnov, omega-square and errors in their application. Politematicheskiy setevoy elektronnyy nauchnyy zhurnal Kubanskogo gosudarstvennogo agrarnogo universiteta [Polythematic network electronic scientific journal of the Kuban State Agrarian University], 2014;(97).

10. Kaida A.Yu., Rybachenko I.A. Checking the consistency of data set attributes with the normal distribution law. Molodezh i sovremennye informatsionnye tekhnologii: sbornik trudov XVIII Mezhdunarodnoy nauchno-prakticheskoy konferentsii studentov, aspirantov i molodyh uchyonyh [Youth and modern information technologies. Proceedings of the XVIII International scientific and practical conference of students, postgraduate students and young scientists]. Tomsk, 2021, pp. 351-352. (In Russ.)

11. Ivanov A.I., Vjatchanin S.E., Malygina E.A., Lukin V.S. Precision statistics: neuroet networking of Chi-square test and Shapiro-Wilk test in the analysis of small samples of biometric data. Reliability and quality of complex systems. 2019;(1):27-34.

12. Lemeshko B.Yu., Rogozhnikov A.P. On the normality of measurement errors in classical experiments and the power of criteria used to check for deviations from the normal law. Metrologiya [Metrology], 2012;(5):3-26. (In Russ.)

13. Gapeeva V.D., Tsybenko B.A., Fayustov A.A. Filtering out gross errors in measurement results using various criteria in the Excel environment. Molodoy ucheniy [Young scientist], 2021;(49):20-27.

14. Mironychev V.N., Titov P.L. Metrologiya, standartizatsiya i sertifikatsiya [Metrology, standardization and certification]. Vladivostok: FEFU, 2015, pp. 141.

15. Pender P.A. Law of ordinal random error: The Rasch measurement model and random error distributions of ordinal assessments. Measurement. 2019:771-781.

16. Simkin G.S. Normal law of measurement error distribution. Measurement Techniques. 2024:271-271.

17. Bobryshov A.P., Solenyi S.V., Serzhantova M.V., Kuzmenko V.P., Sozdateleva M.E., Rudakov R.V. Theoretical assessment of the influence of automation on the production process of verification of measuring instruments. Yadernaya fizika i inzhiniring [Nuclear Physics and Engineering], 2023;(6):571 - 577.

18. Barinova O.A., Nazarov V.N. Metrological certification and verification of error. Verification of measuring instruments. The influence of verification error on the assessment of suitability. Design of operational verification characteristics. Quality criteria for verification of measuring instruments. Methodical instructions for the implementation of a set of laboratory and practical studies. Saint Petersburg, 2009, 15 p.