Параметры программного обеспечения, оказывающие влияние на надежность обработки телеметрической информации

I. N. Kartsan

doi:10.47813/2782-2818-2023-3-4-0322-0331

Authors

I. N. Kartsan https://orcid.org/0000-0003-1833-4036

DOI:

https://doi.org/10.47813/2782-2818-2023-3-4-0322-0331

Keywords:

telemetry information, processing volumes, fault tolerance, multiversionality, software, redundancy.

Abstract

Creation of any modern complex technical systems, as well as their management, is impossible without complex software. In this case, the most important technical task is to increase its reliability and fault tolerance. An important task is to reduce the cost of such systems, as well as to simplify their operation. Software fault-tolerance algorithms have a clear impact on real-time performance: the need to reserve computational resources and time to perform recovery procedures complicates the scheduling process. Several state-of-the-art fault-tolerance solutions exist, and many real-time scheduling algorithms have been developed over the last 40 years. However, analyzing the characteristics of fault-tolerant real-time algorithms and improving scheduling in this area has received less attention from the research community. In this paper, the current state of the art of fault-tolerant approaches for real-time systems is reviewed, and the main challenges and interrelationships are identified, since telemetry is complex time series data. Given the temporal and spatial characteristics of the data, prediction-based anomaly detection methods usually give better results due to the possibility of temporal slope. It is generally believed that the difference between predicted data and real data is small for conventional telemetry data.

Author Biography

I. N. Kartsan

Igor Kartsan, Dr. Sc., Docent, Leading Researcher, Marine Hydrophysical Institute, Russian Academy of Sciences, Sevastopol, Russia

References

Lyu M.R. Software Fault Tolerance. John Wiley & Sons, Inc., 1995. 354 p.

Yakovyna V., Symets I. Reliability assessment of CubeSat nanosatellites flight software by high-order Markov chains, Procedia Computer Science, 2021, 192, 447–456. https://doi.org/10.1016/j.procs.2021.08.046

Laprie J.C., Kanoun K. "X-ware reliability and availability modeling." IEEE Transactions on Software Engineering, 1992, 18(10), 130–147. https://doi.org/10.1109/32.121755

Kartsan I.N., Goncharov A.E., Zelenkov P.V., Kovalev I.V., Fateev Y.L., Tyapkin V.N., Dmitriev D.D. Applying filtering for determining the angular orientation of spinning objects during interference, IOP Conf. Ser.: Mater. Sci. Eng., 2016, 155, 012020. https://doi.org/10.1088/1757-899X/155/1/012020

Shamshad A., Bawadi M.A., Wan Hussin W.M.A., Majid T.A. First and second order Markov chain models for synthetic generation of wind speed time series, S.A.M. Sanusi Energy, 2005, 30(5), 693–708. https://doi.org/10.1016/j.energy.2004.05.026

Yakovyna V., Seniv M., Symets I., Sambir N. () Algorithms and software suite for reliability assessment of complex technical systems, Radio Electronics, Computer Science, Control, 2020, 4, 163–177. https://doi.org/10.15588/1607-3274-2020-4-16

Gruzenkin D.V., Chernigovskiy A.S., Tsarev R.Y. N-version software module requirements to grant the software execution fault-tolerance, Advances in Intelligent Systems and Computing, 2018, 661, 293-303. https://doi.org/10.1007/978-3-319-67618-0_27

Kovalev I.V., Kovalev D.I., Chefonov N.S., Testoedvov N.A., Golovenkin E.N. Implementation of multiversion software based on an object-oriented approach, IOP Conference Series: Materials Science and Engineering, 2020, 734, 12035. https://doi.org/10.1088/1757-899X/734/1/012035

Соловьев Е.В. Система формирования состава мультиверсионного программного обеспечения в реальном времени, Вестник Сибирского государственного аэрокосмического университета им. академика МФ Решетнева, 2014, 2(54), 76-79.

Saramud M.V., Kovalev I.V., Losev V.V., Petrosyan M.O., Kovalev D.I. To the question of applying the correctness calculation methods for assessing the ultimate reliability of multiversion models of fault-tolerant systems, Devices and systems. Management, control, diagnostics, 2019, 3, 19-25.

Yang L., Ma Y., Zeng F., Peng X., Liu D. Improved deep learning based telemetry data anomaly detection to enhance spacecraft operation reliability, Microelectronics Reliability, 2021, 126, 114311. https://doi.org/10.1016/j.microrel.2021.114311.

Карасева М.В., Карцан И.Н., Зеленков П.В. Метапоисковая мультилингвистическая система, Вестник Сибирского государственного аэрокосмического университета им. академика М.Ф. Решетнева, 2007, 3(16), 69-70.

Timchenko, I.E., Naumenko, I.P. Igumnova, E.M., Assimilation of Satellite Observations of the Chlorophyll-a Concentration and the Calculated Data on the Marine Environment Dynamics in the Adaptive Model of the Ecosystem of the Black Sea Northwestern Shelf. Physical Oceanography, 2018, 25(6), pp. 509-520. https://doi.org/10.22449/1573-160X-2018-6-509-520

REFERENCES

Lyu M.R. Software Fault Tolerance. John Wiley & Sons, Inc., 1995. 354 p.

Yakovyna V., Symets I. Reliability assessment of CubeSat nanosatellites flight software by high-order Markov chains, Procedia Computer Science, 2021, 192, 447–456. https://doi.org/10.1016/j.procs.2021.08.046 DOI: https://doi.org/10.1016/j.procs.2021.08.046

Laprie J.C., Kanoun K. "X-ware reliability and availability modeling." IEEE Transactions on Software Engineering, 1992, 18(10), 130–147. https://doi.org/10.1109/32.121755 DOI: https://doi.org/10.1109/32.121755

Kartsan I.N., Goncharov A.E., Zelenkov P.V., Kovalev I.V., Fateev Y.L., Tyapkin V.N., Dmitriev D.D. Applying filtering for determining the angular orientation of spinning objects during interference, IOP Conf. Ser.: Mater. Sci. Eng., 2016, 155, 012020. https://doi.org/10.1088/1757-899X/155/1/012020 DOI: https://doi.org/10.1088/1757-899X/155/1/012020

Shamshad A., Bawadi M.A., Wan Hussin W.M.A., Majid T.A. First and second order Markov chain models for synthetic generation of wind speed time series, S.A.M. Sanusi Energy, 2005, 30(5), 693–708. https://doi.org/10.1016/j.energy.2004.05.026 DOI: https://doi.org/10.1016/j.energy.2004.05.026

Yakovyna V., Seniv M., Symets I., Sambir N. () Algorithms and software suite for reliability assessment of complex technical systems, Radio Electronics, Computer Science, Control, 2020, 4, 163–177. https://doi.org/10.15588/1607-3274-2020-4-16 DOI: https://doi.org/10.15588/1607-3274-2020-4-16

Gruzenkin D.V., Chernigovskiy A.S., Tsarev R.Y. N-version software module requirements to grant the software execution fault-tolerance, Advances in Intelligent Systems and Computing, 2018, 661, 293-303. https://doi.org/10.1007/978-3-319-67618-0_27 DOI: https://doi.org/10.1007/978-3-319-67618-0_27

Kovalev I.V., Kovalev D.I., Chefonov N.S., Testoedvov N.A., Golovenkin E.N. Implementation of multiversion software based on an object-oriented approach, IOP Conference Series: Materials Science and Engineering, 2020, 734, 12035. https://doi.org/10.1088/1757-899X/734/1/012035 DOI: https://doi.org/10.1088/1757-899X/734/1/012035

Soloviev E.V. System of formation of composition of multiversion software in real time, Bulletin of Siberian State Aerospace University named after Academician M.F. Reshetnev, 2014, 2(54), 76-79. (in Russian)

Saramud M.V., Kovalev I.V., Losev V.V., Petrosyan M.O., Kovalev D.I. To the question of applying the correctness calculation methods for assessing the ultimate reliability of multiversion models of fault-tolerant systems, Devices and systems. Management, control, diagnostics, 2019, 3, 19-25.

Yang L., Ma Y., Zeng F., Peng X., Liu D. Improved deep learning based telemetry data anomaly detection to enhance spacecraft operation reliability, Microelectronics Reliability, 2021, 126, 114311. https://doi.org/10.1016/j.microrel.2021.114311. DOI: https://doi.org/10.1016/j.microrel.2021.114311

M.V. Karaseva, I.N. Kartsan, P.V. Zelenkov, Metapearch multilingual system, Vestnik of Siberian State Aerospace University named after academician M.F. Reshetnev, 2007, 3(16), 69-70. (in Russian)

Timchenko, I.E., Naumenko, I.P. Igumnova, E.M., Assimilation of Satellite Observations of the Chlorophyll-a Concentration and the Calculated Data on the Marine Environment Dynamics in the Adaptive Model of the Ecosystem of the Black Sea Northwestern Shelf. Physical Oceanography, 2018, 25(6), pp. 509-520. https://doi.org/10.22449/1573-160X-2018-6-509-520 DOI: https://doi.org/10.22449/1573-160X-2018-6-509-520