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Integrated Impact Model on Critical Infrastructure Safety Related to Its Operation Process Including Operating Environment Threats

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Języki publikacji
EN
Abstrakty
EN
The main aim of this paper is to present the general safety analytical models of complex multistate technical systems related to their operation processes including operating environment threats. In the case of critical infrastructure, the determination of its safety function and its risk function, which graph corresponds to the fragility curve are defined. Moreover, its mean lifetime up to the exceeding a critical safety state and the moment when its risk function value exceeds the acceptable safety level and the critical infrastructure and its components intensities of degradation and the coefficients of operation process including operating environment threats influence on the critical infrastructure and its components intensities of degradation are introduced as the other significant safety indicators. The proposed models are given for multistate series, parallel, “m out of n”, consecutive “m out of n: F”, series-parallel, parallel-series, series-“m out of k”, “mi out of li”-series, seriesconsecutive “m out of k: F” and consecutive “mi out of li”: F”-series systems.
Słowa kluczowe
Rocznik
Strony
11--20
Opis fizyczny
Bibliogr. 11 poz., wykr.
Twórcy
  • Gdynia Maritime University, Gdynia, Poland
  • Gdynia Maritime University, Gdynia, Poland
  • Gdynia Maritime University, Gdynia, Poland
Bibliografia
  • 1. EU-CIRCLE Report D3.3-GMU3, Modelling inside and outside dependences influence on safety of complex multistate ageing systems (critical infrastructures) – Integrated Model of Critical Infrastructure Safety (IMCIS) related to its operation process including operating environment threats (with other critical infrastructures influence, without climate-weather change influence), 2016
  • 2. EU-CIRCLE Report D3.3-GMU3-IMCIS Model1, Integrated Model of Critical Infrastructure Safety (IMCIS) Related to Its Operation Process (OP), Including Operating Environment Threats (OET), IMCIS Model 1, 2016
  • 3. Ferreira F., Pacheco A., Comparison of levelcrossing times for Markov and semi-Markov processes. Statistics and Probability Letters, Vol. 7, No 2, 151-157, 2007
  • 4. Glynn P.W., Haas P.J., Laws of large numbers and functional central limit theorems for generalized semi-Markov processes. Stochastic Models,Vol. 22, No 2, 201-231, 2006
  • 5. Grabski F., Semi-Markov Processes: Application in System Reliability and Maintenance, Amsterdam, Boston, Heidelberd, London, New York, Oxford, Paris, San Diego, San Francisco, Singapore, Sidney, Tokyo, Elsevier, 2014
  • 6. Kołowrocki K., Reliability of Large and Complex Systems, Amsterdam, Boston, Heidelberd, London, New York, Oxford, Paris, San Diego, San Francisco, Singapore, Sidney, Tokyo, Elsevier, 2014
  • 7. Kołowrocki K., Soszyńska-Budny J., Reliability and Safety of Complex Technical Systems and Processes: Modeling - Identification - Prediction - Optimization, London, Dordrecht, Heildeberg, New York, Springer, 2011
  • 8. Limnios N., Oprisan G., Semi-Markov Processes and Reliability. Birkhauser, Boston, 2005
  • 9. Xue J., On multi-state system analysis, IEEE Trans on Reliab. 34, 329-337, 1985
  • 10. Xue J., Yang K., Dynamic reliability analysis of coherent multi-state systems, IEEE Trans on Reliab. 4(44), 683-688, 1995a
  • 11. Xue J., Yang K., Symmetric relations in multi-state systems, IEEE Trans on Reliab 4(44), 689-693, 1995
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-be1cabf8-b84e-4395-9c5b-4b3bc64838f2
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