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Cluster Computing

, Volume 22, Supplement 4, pp 9415–9423 | Cite as

A reliability analysis method of network structure reconfiguration for phased mission system

  • Yumei Wu
  • Jingxiu Yao
  • Bin LiuEmail author
Article
  • 62 Downloads

Abstract

The analytical hierarchy method for phased mission system is presented to construct the time super-network model. The means of network structure reconfiguration are analyzed to improve the reliability including survivability and the invulnerability of the time super-network model for phased mission system. The effects of the reliability of the time super-network by applying network structure reconfiguration are compared. Experiment results show that the survivability and the invulnerability of the network increase as the increasing of the connectivity between new functional unit and others.

Keywords

Reliability System reconfiguration Complex network Phased mission system 

Notes

Acknowledgements

This work has been supported by the National Natural Science Foundation of China (61,503,011).

References

  1. 1.
    Xing, L., Dugan, J.B.: Analysis of generalized phased-mission system reliability, performance, and sensitivity. IEEE Trans. Reliab. 51(2), 199–211 (2002)CrossRefGoogle Scholar
  2. 2.
    Esary, J.D., Ziehms, H.: Reliability analysis of phased missions. Int. J. Reliab. Qual. Saf. Eng. 02(4), 213–236 (1975)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Bechta Dugan, J.: Automated analysis of phased-mission reliability. IEEE Trans. Reliab. 40(1), 45–52 (1991)CrossRefGoogle Scholar
  4. 4.
    Hua, Y., Kui, W.: Reliability analysis of phased-mission system using markov approach. J. Weapon. Equip. Eng. 37(6), 92–96 (2016)Google Scholar
  5. 5.
    Yu, H., Wu, X.: A Petri net software for mission reliability evaluation of PMS. In: Control and Decision Conference, pp. 6040–6044 (2015)Google Scholar
  6. 6.
    Olmez, A.E.: Mission centric reliability analysis of C4ISR architectures using Petri Nets. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 587–592 (2003)Google Scholar
  7. 7.
    Alsafi, Y., Vyatkin, V.: Ontology-based reconfiguration agent for intelligent mechatronic systems in flexible manufacturing. Robot. Comput. Integr. Manuf. 26(4), 381–391 (2010)CrossRefGoogle Scholar
  8. 8.
    Lee, C., Liu, C., Mehrotra, S., Bie, Z.: Robust distribution network reconfiguration. IEEE Trans. Smart. Grid. 6(2), 836–842 (2015)CrossRefGoogle Scholar
  9. 9.
    Sedighizadeh, M., Esmaili, M., Esmaeili, M.: Application of the hybrid big bang-big crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems. Energy 76, 920–930 (2014)CrossRefGoogle Scholar
  10. 10.
    Amanulla, B., Chakrabarti, S., Singh, S.N.: Reconfiguration of power distribution systems considering reliability and power loss. IEEE Trans. Power. Deliv. 27(2), 918–926 (2012)CrossRefGoogle Scholar
  11. 11.
    Kavousi-Fard, A., Niknam, T.: Multi-objective stochastic distribution feeder reconfiguration from the reliability point of view. Energy 64(1), 342–354 (2014)CrossRefGoogle Scholar
  12. 12.
    Szczodrak, M., Gnawali, O., Carloni, L.P.: Dynamic reconfiguration of wireless sensor networks to support heterogeneous applications. In: IEEE International Conference on Distributed Computing in Sensor Systems, vol. 19, pp. 52–61 (2013)Google Scholar
  13. 13.
    Ma, R., Zhu, J., Yang, M.: Reliability analysis of military communication network based on improved clustering survivability. Ordnance Ind. Autom. 31(6), 54–57 (2012)Google Scholar
  14. 14.
    Ma, R., Zhu, J., Yang, M.: Analysis on reliability and nodal importance of military communication network based on invulnerability. Ordnance Ind. Autom. 31(10), 44–59 (2012)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Reliability and Systems EngineeringBeihang UniversityBeijingChina

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