Planning of random statistical monitoring of spacecraft elements during operational testing


Аuthors

Osipov N. A.*, Musienko A. S.**

Mlitary spaсe Aсademy named after A.F. Mozhaisky, 197198, St. Petersburg, Zhdanovskaya St., 13

*e-mail: bayes@mail.ru
**e-mail: vka@mil.ru

Abstract

In the conditions of strict requirements for reliability and limited resources during testing of spacecraft components, an urgent task is to optimize the volume of random control without reducing the reliability of decisions on the suitability of product batches. This article discusses an approach to solving this problem based on the systematic application of statistical hypothesis testing methods. The main objective of the study is to develop a methodology that can significantly reduce the volume of necessary random tests through the intelligent use of a priori information.

The article considers the application of statistical hypothesis testing methods to reduce the volume of random control during control tests of spacecraft components and to obtain a more reliable decision on the suitability of a batch. The main advantage of the proposed approach is the ability to take into account the results of previous tests of analog products, similar products and products with similar components.

The purpose of the study is to reduce the number of testing stages to identify the absence of accidents and failures when a malfunction of the on-board system occurs. To achieve this goal, it is proposed to use an original heuristic algorithm for forming sets of tested products.

The article provides a practical implementation of this approach within the framework of random control procedures. The methodology for processing and integrating statistical data from various sources is described in detail. The methodology is based on the sequential verification of a set of statistical hypotheses concerning the fundamental properties of the probability distribution underlying the observed reliability and failure indicators (such as the type of distribution, its parameters, for example, the mean time between failures or the probability of failure-free operation). The results of the study are of significant practical interest to organizations that carry out control tests of highly reliable and expensive products, in particular, in the space industry, where the requirements for the reliability of solutions are extremely high, and the cost of testing each sample is high.

Keywords:

control tests, sampling plans, consumer risk, statistical hypothesis, statistical test

References

  1. Dorozhko I.V., Musienko A.S. A model for monitoring the technical condition of complex using artificial intelligence. Trudy MAI. 2024. No. 137. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=181885

  2. Dorozhko I.V., Musienko A.S., Sundiev D.S. Simulation model linking reliability indicators with indicators of test and functional control of technical systems. Trudy MAI. 2023. No. 130. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=174618. DOI: 10.34759/trd-2023-130-19

  3. Baranovskii A.M., Musienko A.S. Dynamic diagnostic models and method for ensuring the stability of monitoring the technical condition of on-board control systems of aircraft. Trudy MAI. 2024. No. 139. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=183468

  4. Osipov N.A., Musienko A.S., Merkushev O.A. Reducing the volume of the test sample of spacecraft elements during control tests. Trudy MAI. 2023. No. 132. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=176857

  5. Osipov N.A., Musienko A.S. Increasing the validity of selective control of the on-board system during the operation of space assets. Trudy MAI. 2024. No. 136. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=180675

  6. Kofman A. Vvedenie v teoriyu nechetkikh mnozhestv (Introduction to the Theory of Fuzzy Sets). Moscow: Nauka Publ., 2082. 432 p.

  7. Kokhenderfer M., Uiler T., Rei K. Algoritmy prinyatiya reshenii (Decision-Making Algorithms). Moscow: DMK Press Publ., 2023. 684 p.

  8. Flegontov A.V., Vilkov V.B., Chernykh A.K. Modelirovanie zadach prinyatiya reshenii pri nechetkikh iskhodnykh dannykh (Modeling of Decision-Making Problems with Fuzzy Initial Data). Saint Petersburg: Lan' Publ., 2020. 332 p.

  9. Solov'ev V.A., Lyubinskii V.E., Zhuk E.I. Current state and prospects of development of the flight control system of spacecraft. Pilotiruemye polety v kosmos. 2012. No. 2 (4). P. 44 – 51. (In Russ.)

  10. Storozhev S.A., Khizhnyakov Yu.N. A new status regulator adapting method employing fuzzy logic. Trudy MAI. 2021. No. 118. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=158255. DOI: 10.34759/trd-2021-118-16

  11. Osipov N.A., Dorozhko I.V. Diagnostics of Automated Control Systems for Complex Objects Using A Priori Information. Izvestiya vuzov. Priborostroenie. 2013. Vol. 56, No. 1. P. 18–26. (In Russ.)

  12. Dorozhko I.V. et al. Model for Estimating the Probability of Uninterrupted Operation of Complex Technical Systems Based on Dynamic Bayesian Networks. Trudy Voenno-kosmicheskoi akademii im. A.F. Mozhaiskogo. 2019. No. 669. P. 216– 223. (In Russ.)

  13. Dorozhko I.V. et al. Technical state prediction of complex technical systems by the Berg algorithm and Bayesian networks. Trudy MAI. 2020. No. 113. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=118181. DOI: 10.34759/trd-2020-113-14

  14. Dorozhko I.V., Gorokhov G.M., Kirillov I.A. Methodological approach to the development of a decision support system for the operator of an automated process control system based on dynamic bayesian networks. Trudy MAI. 2022. No. 125. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=168195. DOI: 10.34759/trd-2022-125-23

  15. Dorozhko I.V., Ivanov O.A. Decision-making support system model for spacecraft onboard systems diagnosing based on Bayesian networks. Trudy MAI. 2021. No. 118. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=158259. DOI: 10.34759/trd-2021-118-19

  16. Gusenitsa YA.N., Dorozhko I.V., Kochanov I.A., Petukhov A.B. Scientific-methods approach to evaluating readiness of complex technical systems with account for metrological assurance. Trudy MAI. 2018. No. 98. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=90383

  17. Privalov A.E. et al. Simulation model for assessing the availability of technical systems taking into account characteristics of the diagnostic process. Trudy MAI. 2018. No. 103. (In Russ.). URL: https://trudymai.ru/eng/published.php?ID=101526

  18. Lyashevskii A.V., Astankov A.M., Suvorov A.N., Golovchinskaya N.V. Methodology for predicting the residual resource of technological equipment based on the observed failure rate parameter. Aviakosmicheskoe priborostroenie. 2025. No. 2. (In Russ.). DOI: 10.25791/aviakosmos.2.2025.1463

  19. Tarasov A.G., Musienko A.S. Algorithm of functioning of agents of detection and localization of failures of onboard system. Aviakosmicheskoe priborostroenie. 2024. No. 8. (In Russ.). DOI: 10.25791/aviakosmos.8.2024.1426

  20. Lupashko M.N., Stepanov I.V., Timofeev V.V. Technique of determination of the frequency of use of spare elements of the computing system of military purpose. Trudy Voenno-kosmicheskoi akademii im. A.F. Mozhaiskogo. 2021. No. 678. P. 310–315. (In Russ.)

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