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Tytuł artykułu

Identification methods and procedures of critical infrastructure operation process including operating environment threats

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the paper, there are presented the methods for identification of the critical infrastructure operation process on the basis of statistical data coming from this process realizations related to the critical infrastructure operating environment threats. These are the methods and procedures for estimating the unknown basic parameters of the critical infrastructure operation process semi-Markov model and identifying the distributions of the critical infrastructure operation process conditional critical infrastructure operation process sojourn times at the particular operation states. There are given the formulae estimating the probabilities of the critical infrastructure operation process straying at the particular operation states at the initial moment, the probabilities of the critical infrastructure operation process transitions between the operation states. Moreover, there are given formulae for the estimator of unknown parameters of the distributions suitable and typical for the description of the critical infrastructure operation process conditional sojourn times at the operation states. Namely, the parameters of the uniform distribution, the triangular distribution, the double trapezium distribution, the quasi-trapezium distribution, the exponential distribution, the Weibull’s distribution and the chimney distribution are estimated using the statistical methods such as the method of moments and the maximum likelihood method. The chi-square goodness-of-fit test is described and proposed to be applied to verifying the hypotheses about these distributions choice validity. The procedure of statistical data sets uniformity analysis based on Kolmogorov-Smirnov test is proposed to be applied to the empirical conditional sojourn times at the operation states coming from different realizations of the same critical infrastructure operation process.
Rocznik
Strony
155--168
Opis fizyczny
Bibliogr. 16 poz.
Twórcy
  • Maritime University, Gdynia, Poland
  • Gdynia Maritime University, Gdynia, Poland
Bibliografia
  • [1] EU-CIRCLE Report D2.1-GMU2 (2016), Modelling outside dependences influence on Critical Infrastructure Safety (CIS) – Modelling Critical Infrastructure Operation Process (CIOP) including Operating Environment Threats (OET), 2016
  • [2] EU-CIRCLE Report D2.3-GMU2 (2016), Identification Methods and Procedures of Climate Weather Change Process Including Extreme Weather Hazards
  • [3] EU-CIRCLE Report D3.3-GMU3 (2016), Modelling inside dependences influence on safety of multistate ageing systems – Modelling safety of multistate ageing systems
  • [4] EU-CIRCLE ReportD3.3-GMU12 (2017), Integration of the Integrated Model of Critical Infrastructure Safety (IMCIS) and the Critical Infrastructure Operation Process General Model (CIOPGM) into the General Integrated Model of Critical Infrastructure Safety (GIMCIS) related to operating environment threads (OET) and climateweather extreme hazards (EWH)
  • [5] Gamiz M. L. (2008), Roman Y., Non-parametric estimation of the availability in a general repairable. Reliability Engineering & System Safety, Vol. 93, No. 8, 1188-1196
  • [6] Helvacioglu S., Insel M. (2008) Expert system applications in marine technologies. Ocean Engineering, Vol. 35, No. 11-12, 1067-1074
  • [7] Hryniewicz O. (1995), Lifetime tests for imprecise data and fuzzy reliability requirements. Reliability and Safety Analyses under Fuzziness. Onisawa T. and Kacprzyk J., Eds., Physica Verlag, Heidelberg, 169-182
  • [8] Kołowrocki K. (2004), Reliability of Large Systems, Elsevier, ISBN: 0080444296
  • [9] Kołowrocki K. (2014), Reliability of large and complex systems, Elsevier, ISBN: 978080999494
  • [10] Kolowrocki K., Soszynska-Budny J. (2011), Reliability and Safety of Complex Technical Systems and Processes: Modeling-IdentificationPrediction-Optimization. Springer, ISBN: 9780857296931
  • [11] Limnios N., Oprisan G. (2005), Semi-Markov Processes and Reliability. Birkhauser, Boston
  • [12] Limnios N., Ouhbi B., Sadek A. (2005), Empirical estimator of stationary distribution for semiMarkov processes. Communications in Statistics-Theory and Methods, Vol. 34, No. 4, 987-995 12
  • [13] Macci C. (2008), Large deviations for empirical estimators of the stationary distribution of a semiMarkov process with finite state space. Communications in Statistics-Theory and Methods, Vol. 37, No. 19,3077-3089
  • [14] Mercier S. (2008), Numerical bounds for semi-Markovian quantities and application to reliability. Methodology and Computing in Applied Probability, Vol. 10, No. 2, 179-198
  • [15] Rice J. A. (2007), Mathematical statistics and data analysis. Duxbury. Thomson Brooks/Cole. University of California. Berkeley
  • [16] Vercellis S. (2009), Data mining and optimization for decision making. John Wiley & Sons Ltd.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-519978c6-fec9-4360-9e3b-92d8e68a9c5b
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