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2016 | Vol. 7, No. 3 | 143--148
Tytuł artykułu

Prediction of climate-weather change process for port oil piping transportation system and maritime ferry operating at Baltic Sea area

Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
There are presented the methods of prediction of the climate-weather change process. These are the methods and procedures for estimating the unknown basic parameters of the climate-weather change process semi-Markov model and identifying the distributions of the climate-weather change process conditional sojourn times at the climate-weather states. There are given the formulae estimating the probabilities of the climate-weather change process staying at the particular climate-weather states at the initial moment, the probabilities of the climate-weather change transitions between the climate-weather states and the parameters of the distributions suitable and typical for the description of the climate-weather change process conditional sojourn times at the particular climate-weather states. The proposed statistical methods applications for the unknown parameters identification of the climate-weather change process model determining the climate-weather change process parameters for the initial point of the port oil piping transportation system are presented.
Wydawca

Rocznik
Strony
143--148
Opis fizyczny
Bibliogr. 23 poz., rys.
Twórcy
  • Maritime University, Gdynia, Poland
  • Gdynia Maritime University, Gdynia, Poland
Bibliografia
  • [1] Barbu, V. & Limnios, N. (2006). Empirical estimation for discrete-time semi-Markov processes with applications in reliability. Journal of Nonparametric Statistics 18, 7-8, 483-498.
  • [2] Collet, J. (1996). Some remarks on rare-event approximation. IEEE Transactions on Reliability 45, 106-108.
  • [3] EU-CIRCLE Report D2.1-GMU3. (2016). Modelling Climate-Weather Change Process Including Extreme Weather Hazards.
  • [4] EU-CIRCLE Report D2.3-GMU2. (2016). Identification Methods and Procedures of ClimateWeather Change Process Including Extreme Weather Hazards.
  • [5] EU-CIRCLE Report D3.3-GMU1. (2016). Modelling inside dependences influence on safety of multistate ageing systems – Modelling safety of multistate ageing systems.
  • [6] EU-CIRCLE Report D3.3-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).
  • [7] Gamiz, M. L. & Roman, Y. (2008). Nonparametric estimation of the availability in a general repairable. Reliability Engineering & System Safety 93, 8, 1188-1196.
  • [8] Giudici, P. & Figini, S. (2009). Applied data mining for business and industry. John Wiley & Sons Ltd.
  • [9] Habibullah, M. S., Lumanpauw, E., Kolowrocki, K. et al. (2009). A computational tool for general model of operation processes in industrial systems operation processes. Electronic Journal Reliability & Risk Analysis: Theory & Applications 2, 4, 181191.
  • [10] Helvacioglu, S. & Insel, M. (2008). Expert system applications in marine technologies. Ocean Engineering 35, 11-12, 1067-1074.
  • [11] Hryniewicz, O. (1995). Lifetime tests for imprecise data and fuzzy reliability requirements. Reliability and Safety Analyses under Fuzziness. Onisawa, T. & Kacprzyk, J. (Ed.). Physica Verlag, Heidelberg, 169-182.
  • [12] Jakusik, E., Kołowrocki, K., Kuligowska, E. et al. (2016). Modelling climate-weather change process including extreme weather hazards for oil piping transportation system. Journal of Polish Safety and Reliability Association, Summer Safety & Reliability Seminars 7, 3, 31-40.
  • [13] Kołowrocki, K. (2004). Reliability of Large Systems, Elsevier, ISBN: 0080444296.
  • [14] Kołowrocki, K. (2014). Reliability of large and complex systems, Elsevier, ISBN: 978080999494.
  • [15] Kolowrocki, K. & Soszynska-Budny, J. (2011). Reliability and Safety of Complex Technical Systems and Processes: Modeling-IdentificationPrediction-Optimization. Springer, ISBN: 9780857296931.
  • [16] Limnios, N. & Oprisan, G. (2005). Semi-Markov Processes and Reliability. Birkhauser, Boston.
  • [17] Limnios, N., Ouhbi, B. & Sadek, A. (2005). Empirical estimator of stationary distribution for semi-Markov processes. Communications in Statistics-Theory and Methods 34, 4, 987-995.
  • [18] 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 37, 19, 3077-3089.
  • [19] Mercier, S. (2008). Numerical bounds for semiMarkovian quantities and application to reliability. Methodology and Computing in Applied Probability 10, 2, 179-198.
  • [20] Rice, J. A. (2007). Mathematical statistics and data analysis. Duxbury. Thomson Brooks/Cole. University of California. Berkeley.
  • [21] Soszyńska, J., Kołowrocki, K., Blokus-Roszkowska, A. et al. (2010). Identification of complex technical system components safety models. Journal of Polish Safety and Reliability Association, Summer Safety & Reliability Seminars 4, 2, 399-496.
  • [22] Vercellis, S. (2009). Data mining and optimization for decision making. John Wiley & Sons Ltd.
  • [23] Wilson, A. G., Graves, T. L., Hamada, M. S. et al. (2006). Advances in data combination, analysis and collection for system reliability assessment. Statistical Science 21, 4, 514-531.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-1c7e466d-8f2e-4e20-a38a-f943ae34e40e
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