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A Morphological Approach to Development of a Process for Measurement Uncertainty Estimation

https://doi.org/10.21122/2220-9506-2024-15-2-110-119

Abstract

The problem of increasing the reliability of uncertainty estimation of measurement results is considered. The purpose of this work was to justify the application of the process approach to the formation of the algorithm of uncertainty estimation on the basis of morphological analysis. It is theoretically substantiated that from the standpoint of the system approach to achieving an acceptable degree of reliability of measurement uncertainty estimates it is necessary to implement a process approach to the formation of the estimation method as an algorithm of actions. The main stages of the estimation process are defined. It is established that each stage of the estimation process can be realized by alternative methods. The morphological box method as a realization of morphological analysis is proposed as a basis for its solution. A morphological box design of the uncertainty estimation process with an open architecture is presented, based only on commonly accepted methods and approaches for realizing each step of the process. Two aspects of the application of the morphological box method are identified. On the one hand, the morphological box allows to form an algorithm of the uncertainty assessment process, maximally acceptable for the laboratory conditions, as a combination of process steps, based on the task at hand, combining different variants of realization of these steps. On the other hand, the morphological box acts as a tool for development of new methods of realization of various stages of the uncertainty assessment process. Examples of using the morphological box method to develop alternative algorithms of the uncertainty estimation process of the same measurement method and to develop new methods of realization of different stages of the estimation process are considered.

About the Authors

P. S. Serenkov
Belarusian National Technical University
Belarus

Address for correspondence:
Serenkov P.S.–
Belarusian National Technical University,
Nezavisimosty Ave., 65, Minsk 220013, Belarus
e-mail: pavelserenkov@bntu.by



V. M. Romanchak
Belarusian National Technical University
Belarus

Nezavisimosty Ave., 65, Мinsk 220013, Belarus



A. V. Hrybkouski
Belarusian National Technical University
Belarus

Nezavisimosty Ave., 65, Мinsk 220013, Belarus



References

1. EUROLAB Technical Report 1/2007: Measurement uncertainty revisited: Alternative approaches to uncertainty evaluation. EUROLAB. 2007:62.

2. Serenkov PS, Romanchak VM. Method of successive transformations as an alternative realization of Bayesian approach to estimation of measurement uncertainty. Metrology and instrumentation. 2021;4:9-16. (In Russ.).

3. Efremova NYu. Measurement uncertainty. Creation, current state and prospects for the development of uncertainty concept. Metrology and instrumentation. 2016;3:7-17. (In Russ.).

4. Taylor JR. An introduction to error analysis: the study of uncertainties in physical measurements. Sausalito, California, University of Science Books. 1997;(2):346.

5. Hall BD, White DR. An introduction to measurement uncertainty. New Zealand, Measurement Standards Laboratory of New Zealand. 2017:53.

6. Optner SL. System analysis for the solution of problems of business and industry. Moscow: Concept Publ. 2006:206.

7. Serenkov PS, Gurevich VL, Movlamov VR. Process approach to the study of accuracy indicators of measurement methods. Metrology and instrumentation. 2017;(77)3:100-108. (In Russ.).

8. Ritchey T. On a morphology of theories of emergence. Acta Morphologica Generalis. 2014;(3)3:1-16.

9. Ilyin VN, Lepekhin AV. Automation technology for structural-parametric synthesis based on the morphological box method. Proceedings of the Moscow Aviation Institute. 2011;46:1-11. (In Russ.).

10. Akimov SV. Identification Level Morphological Set Model. Proceedings of educational institutions of communication. 2005;172:120-135. (In Russ.).

11. Minaev AM. Theory and practice of error analysis. Moscow, Sputnik Publ. 2013:507.

12. Measurement Systems Analysis Reference Manual, 3rd edn, Daimler Chrysler Corporation, Ford Motor Company, General Motors Corporation. 2002:238.

13. Rabinovich SG. Measurement errors and uncertainty: theory and practice, 3rd edn. New York. Springer. 2005:308.

14. Prokopov AV, Zakharov IP, Botsyura OA. The main problems of substantiating the model equation when estimating measurement uncertainty. Ukrainian Metrological Journal. 2016;3:19-22. (In Russ.).

15. Dietrich CF. Uncertainty, calibration and probability: the statistics of scientific and industrial measurement. Routledge. CRC Press Publ. 199;555.

16. Cox M, Harris P, Siebert B. Estimation of measurement uncertainty based on distribution transformation using Monte Carlo simulation. Measurement Techniques. 2003;46:824-833. DOI: 10.1023/B:METE.0000008439.82231.ad

17. Lira I. Evaluating the Measurement Uncertainty. Fundaments and Practical Guidance. Bristol. Institute of Physics Publ. 2002;13(9):243. DOI: 10.1088/0957-0233/13/9/709

18. Bich W. Revision of the «Guide to the expression of uncertainty in measurement». Why and how. Metrologia. 2014;51(4):155–158. DOI: 10.1088/0026-1394/51/4/S155

19. Serenkov PS, Hurevich VL, Tolochko TK. Features of application of a combined approach to the evaluation of the measurement results uncertainty. Instruments and measurement methods. 2020;11(1):60-69. DOI: 10.21122/2220-9506-2020-11-1-60-69

20. Serenkov PS. Application of nonparametric models for estimating uncertainties in measurement results. Bulletin of the Belarusian – Russian University. 2015;48(3):109-115.


Review

For citations:


Serenkov P.S., Romanchak V.M., Hrybkouski A.V. A Morphological Approach to Development of a Process for Measurement Uncertainty Estimation. Devices and Methods of Measurements. 2024;15(2):110-119. https://doi.org/10.21122/2220-9506-2024-15-2-110-119

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ISSN 2220-9506 (Print)
ISSN 2414-0473 (Online)