Statistical modeling method for designing information and measurement control systems taking into account features of stochastic processes

by

K.S. Tkachenko, I.A. Skatkov

FSAEI HE “Sevastopol State University”, RF, Sevastopol, Universitetskaia St., 33

E–mail: KSTkachenko@sevsu.ru

DOI: 10.33075/2220-5861-2020-1-46-53

UDC 504.064+004.75

Abstract:

     Existing measuring devices may deteriorate their metrological characteristics due to their operation under high load and in adverse environmental conditions. Their design should take into account the possibility of significant distortion of input influences. It is difficult to take into consideration these distortions using linear additive models that do not take into account the drift of the measured value, which leads to degradation of measuring instruments. Therefore, this article proposes an approach for analytical evaluation of the characteristics of measuring devices using statistical approximation of Ito processes. Based on the results of this approximation, the construction of fundamentally new, advanced measuring devices can be made.

     Let the primary meter with degradation be represented as a single-channel queuing system with a limited queue, the counts of the intensity of degradation events taken at regular intervals are known. Then, to estimate the probability of the lack of necessity for repair and maintenance, it is required to approximate a random process and construct the Ito equation. However, changes can affect not only the intensity of the input stream, but also the processing performance. Then the performance is presented as a function of a random walk from time-a continuous Ito process. Modeling a continuous Ito process describes performance degradation.

     This approach makes it possible to obtain point analytical characteristics of the measuring device taking into account the statistical features inherent to the input degradation stochastic flow. On the basis of these characteristics, it is possible to build meters taking into account the degradation processes in them by predicting the uptime and planning preventive work. The proposed method is scaled without difficulty for the cases of multi-core multiprocessor systems.

Keywords: Ito process, statistical estimates.

To quote, follow the DOI link and use the Actions-Cite option or copy:

[IEEE] K. S. Tkachenko and I. A. Skatkov, “Statistical modeling method for designing information and measurement control systems taking into account features of stochastic processes,” Monitoring systems of environment, no. 1, pp. 46–53, Mar. 2020.

Full text in PDF(RUS)

LIST OF REFERENCES

  1. Shivrinskii V.N. Measurement of static ambient pressure on a moving object // Bulletin of the Ulyanovsk State Technical University. 2004. № 2 (26). P. 46–50.
  2. Rybnov Iu.S. Full-scale measurements of the infrasound impact of mining machinery and equipment on the environment / Iu.S. Rybnov, N.M. Syrnikov, S.Iu. Rybnov [and others] // Mining Information and Analytical Bulletin (scientific and technical journal). 2009. № 6. P. 255–260.
  3. Chernyshova T.I., Nistratov M.I. Assessment of metrological resource of thermophysical measurement tools taking into account the influence of the environment // Bulletin of Tambov State Technical University. 2008. V. 14. № 3. P. 473–477.
  4. Semenov S.Iu., Sterlikov A.V. Organization of inter-laboratory comparison tests in laboratories that perform direct measurements on environmental objects // Population Health and Habitat. 2018. № 3 (300). P. 43–48.
  5. Starkova O.S. Weak and generalized random variable solution of the stochastic Cauchy problem with additive noise // Bulletin of the South Ural State University. Series: Mathematics. Mechanics. Physics. 2016. V. 8. № 1. P. 19–27.
  6. Nasyrov F.S. On the generalized Tanaka formula // Ufa Mathematical Journal. 2009. V. 1. № 1. P. 69–76.
  7. Aleksandrova O.V. Symmetry and first integrals of systems of stochastic differential equations Ito // Bulletin of the Yaroslav-the-Wise Novgorod State University. 2013. V. 1. № 75. P. 54–59.
  8. Saichev A.I., Filimonov V.A., Tarakanova M.V. Estimation of the diffusion coefficient of a Wiener random process with uniform drift // Bulletin of the Nikolai Lobachevsky Nizhniy Novgorod University. 2010. № 5–1. P. 61–66.
  9. Tkachenko K.S., Skatkov I.A., Skidan A.A. Model of functioning of the primary meter in the conditions of the trend of metrological characteristics // Environmental, industrial and energy security – 2017. Collection of articles based on the materials of the scientific and practical conference with international participation. Under the ed. of Iu.A. Omelchuk. 2017. P. 1349–1353.
  10. Tkachenko K.S. Determining the probabilities of hypotheses about the state of the primary meter with degradation // Mat. of IV SPIC «Environmental problems of the Azov-Black Sea region and integrated management of biological resources». Ed.: Kolorit. 2017. P. 252–256.
  11. Stepanov S.S. Stochastic world. URL: http://synset.com/pdf/ito.pdf (date of issue: 09.12.2019).

Loading