Identification methods and procedures of climate-weather change process including extreme weather hazards of port oil piping transportation system operating at land Baltic seaside area

• Autorzy: Jakusik E. , Kołowrocki K. , Kuligowska E. , Soszyńska-Budny J. , Torbicki M.
• Języki publikacji: EN

Abstrakty

EN

There are presented the methods of identification 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 port oil piping transportation system operating at land Baltic seaside area are presented.

Słowa kluczowe

EN

climate-weather change process; semi-Markov model; modelling; identification; transportation system

• Wydawca: Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Journal of Polish Safety and Reliability Association
• Rocznik: 2016
• Tom: Vol. 7, No. 3
• Strony: 57--64
• Opis fizyczny: Bibliogr. 24 poz., tab., wykr.

Twórcy

autor

Jakusik E.

• Institute of Meteorology and Water Management - NRI, Warsaw, Poland

autor

Kołowrocki K.

• Maritime University, Gdynia, Poland

autor

Kuligowska E.

• Maritime University, Gdynia, Poland

autor

Soszyńska-Budny J.

• Maritime University, Gdynia, Poland

autor

Torbicki M.

• 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 Climate-Weather 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 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 climate-weather 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, 181-191.
• [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). Identification methods and procedures of climate-weather change process including extreme weather hazards for oil piping transportation system operating at under water Baltic Sea area, Summer Safety & Reliability Seminars. Journal of Polish Safety and Reliability Association 7, 3, 47-56.
• [13] Jakusik, E., Kołowrocki, K., Kuligowska, E. et al. (2016). Modelling climate-weather change process including extreme weather hazards for oil piping transportation system, Summer Safety & Reliability Seminars. Journal of Polish Safety and Reliability Association 7, 3, 31-40.
• [14] Kołowrocki, K. (2004). Reliability of Large Systems, Elsevier, ISBN: 0080444296.
• [15] Kołowrocki, K. (2014). Reliability of large and complex systems, Elsevier, ISBN: 978080999494.
• [16] Kolowrocki, K. & Soszynska-Budny, J. (2011). Reliability and Safety of Complex Technical Systems and Processes: Modeling-Identification Prediction-Optimization. Springer, ISBN: 9780857296931.
• [17] Limnios, N. & Oprisan, G. (2005). Semi-Markov Processes and Reliability. Birkhauser, Boston.
• [18] 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.
• [19] Macci, C. (2008). Large deviations for empirical estimators of the stationary distribution of a semi-Markov process with finite state space. Communications in Statistics-Theory and Methods 37, 19,3077-3089.
• [20] Mercier, S. (2008). Numerical bounds for semi-Markovian quantities and application to reliability. Methodology and Computing in Applied Probability 10, 2, 179-198.
• [21] Rice, J. A. (2007). Mathematical statistics and data analysis. Duxbury. Thomson Brooks/Cole. University of California. Berkeley.
• [22] Soszyńska, J., Kołowrocki, K., Blokus-Roszkowska, A. et al. (2010). Identification of complex technical system components safety models. Summer Safety & Reliability Seminars. Journal of Polish Safety and Reliability Association 42, 399-496.
• [23] Vercellis, S. (2009). Data mining and optimization for decision making. John Wiley & Sons Ltd.
• [24] 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.