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Automated Logical-Probabilistic Methodology and Software Tool as Component of the Complex of Methodologies and Software Tools for Evaluation of Reliability and Survivability of Onboard Equipment of Small Satellites

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Software Engineering and Algorithms in Intelligent Systems (CSOC2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 763))

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Abstract

The paper presents solutions for current problems with estimation and analysis of indicators of reliability and survivability in onboard equipment (OE) of small satellites (SS) based on the logical-probabilistic approach to the reliability and survivability estimation of complex systems. There were developed modified logical-probabilistic method and software tool for evaluating the reliability and survivability of OE SS systems. The correctness of suggested method and software tool was shown by computational experiments on some systems of OE SS similar to Belarusian SS, later compared with “Arbitr” software complex results. The software tool was integrated into the complex of methodologies and software tools for evaluation, analysis and prediction of the values of reliability and survivability indicators of OE SS in local desktop and distributed web versions.

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References

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Acknowledgments

The research described in Sect. 2 of paper is supported by project No. 17-11-01254 of Russian Science Foundation, the research described in Sect. 3 of paper is supported by the state research 0073–2018–0003. All represented in paper research results were supported by Program STC of Union State “Monitoring-SG” (project 6MCГ/13-224-2, the Belarusian side).

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Correspondence to Vadim Skobtsov .

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Skobtsov, V., Lapitskaja, N., Saksonov, R., Potryasaev, S. (2019). Automated Logical-Probabilistic Methodology and Software Tool as Component of the Complex of Methodologies and Software Tools for Evaluation of Reliability and Survivability of Onboard Equipment of Small Satellites. In: Silhavy, R. (eds) Software Engineering and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 763. Springer, Cham. https://doi.org/10.1007/978-3-319-91186-1_47

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