Global Sensitivity Analysis of Multi-State Markov Reliability Models of Power Equipment Approximated by Polynomial Chaos Expansion / Analiza Globalnej Wrażliwości Wielostanowych Modeli Niezawodności Markowa Dla Urządzeń Energetycznych Aproksymowanych Za Pomocą Rozwinięcia W Chaos Wielomianowy

[1] Billinton R., Allan R.: "Reliability Evaluation of Engineering Systems", Second Edition, Plenum Press 199210.1007/978-1-4899-0685-4Search in Google Scholar

[2] Lisnianski A., Elmakias D., Laredo D., BenHaim H.: A multi-state Markov model for a short-term reliability analysis of a power generating unit, Reliability Engineering and System Safety, 2012, 98, ,1-610.1016/j.ress.2011.10.008Search in Google Scholar

[3] Billinton R, Fotuhi-Firuzabad M, Sidhu TS.: Determination of the optimum routine test and self checking intervals in protective relaying using a reliability model. IEEE Trans Power Syst 2002; 17(3).10.1109/TPWRS.2002.800871Search in Google Scholar

[4] Seyedi H, Fotuhi M, Sanaye-Pasand M.: An extended Markov model to determine the reliability of protective system. In: Power India conference, IEEE; April 1, 2006. p. 10-12.10.1109/POWERI.2006.1632549Search in Google Scholar

[5] Aminifar F, Firuzabad MF, Billinton R.: Extended reliability model of a unified power flow controller. Gener Transm Distrib IET 2007;1(6):896-903.10.1049/iet-gtd:20070089Search in Google Scholar

[6] Sefidgaran M., Mirzaie M., Ebrahimzadeh A.: Reliability model of the power transformer with ONAF cooling, Electrical Power and Energy Systems 35 (2012) 97-104Search in Google Scholar

[7] Do Van P., Barros A., Bérenguer C.: Reliability importance analysis of Markovian systems at steady state using perturbation analysis, Reliability Engineering and System Safety, 2008, 93, 1605-1615.10.1016/j.ress.2008.02.020Search in Google Scholar

[8] Do Van P., Barros A., Bérenguer C.: From differential to difference importance measures for Markov reliability models, European Journal of Operational Research, 2010, 513-52110.1016/j.ejor.2009.11.025Search in Google Scholar

[9] Zio E.: Computational Methods For Reliability And Risk Analysis, Series on Quality, Reliability & Engineering Statistics Vol 14, World Scientific Publishing Company, 200910.1142/7190Search in Google Scholar

[10] Marseguerra M., Padovani E., Zio E., Tarantola S., Saltelli A.: Sensitivity Analysis of a non-linear reliability model. Proceeding of the European Conference on Safety and Reliability ESREL 1998, Norway, 1998, pp. 1395-1401.Search in Google Scholar

[11] Aperjis, D., White D.C., Schweppe F.C., Mettler M., Merrill H.M.: Energy Strategy Planning for Electric Utilities Part II, Smarte Methodology Case Study. IEEE Transactions on Power Apparatus and Systems 1982, PAS-101(2): 347-355.10.1109/TPAS.1982.317113Search in Google Scholar

[12] Merrill, H.M. and Schweppe F.C.: Energy Strategy Planning for Electric Utilities Part II, Smarte Methodology. IEEE Transactions on Power Apparatus and Systems, 1982, PAS-101(2): 340-346.10.1109/TPAS.1982.317112Search in Google Scholar

[13] Rocco C.M.: Effects of transition-rate uncertainty on steady-state probabilities of Markov models using Interval Arithmetic, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability April 2012 vol. 226 no. 2 234-24510.1177/1748006X11422624Search in Google Scholar

[14] Rocco C.M.: “Affine Arithmetic for assessing the uncertainty propagation on steady-state probabilities of Markov models due to uncertainties in transition rates”, submitted to Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 201210.1177/1748006X13485189Search in Google Scholar

[15] Saltelli A., Chan K., Scott E.M.: “Sensitivity Analysis”, John Wiley & Sons, Probability and Statistics series, 2000Search in Google Scholar

[16] Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.: “Sensitivity Analysis in Practice. A Guide to Assessing Scientific Models”, John Wiley & Sons, Probability and Statistics series, 2004Search in Google Scholar

[17] Campolongo F., Saltelli A., Cariboni J.: From screening to quantitative sensitivity analysis. A unified approach Computer Physics Communications 182 (2011) 978-988Search in Google Scholar

[18] Haro E., Anstett-Collin F., Basset M., Sensitivity study of dynamic systems using polynomial Chaos, Reliability Engineering and System Safety, 2012, 104 , pp. 15-2610.1016/j.ress.2012.04.001Search in Google Scholar

[19] Ghanem R., Spanos P. D., Polynomial chaos in stochastic finite elements, Journal of Applied Mechanics 57 (1990) 197-202.10.1115/1.2888303Search in Google Scholar

[20] Sudret B., Global sensitivity analysis using polynomial chaos expansions, Reliability Engineering and System Safety 93 (7) (2008) 964-979.10.1016/j.ress.2007.04.002Search in Google Scholar

[21] Crestaux T., Maitre O. L, Martinez J., Polynomial chaos expansion for sensitivity analysis, Reliability Engineering & System Safety 94 (2009) 1161-1172.10.1016/j.ress.2008.10.008Search in Google Scholar

[22] Oladyshkin S., Class H., Helmig R., Nowak W., A concept for data-driven uncertainty quantification and its application to carbon dioxide storage in geological formations, Advances in Water Resources 34 (2011) 1508-1518. doi:10.1016/j.advwatres.2011.08.005.10.1016/j.advwatres.2011.08.005Search in Google Scholar

[23] Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D. Saisana, M., Tarantola, S.: “Global Sensitivity Analysis: The Primer”, John Wiley & Sons, 200810.1002/9780470725184Search in Google Scholar

[24] Wiener N., The homogeneous chaos, American Journal of Mathematics 60 (4) (1938) 897-936. 10.2307/2371268Search in Google Scholar

[25] Xiu D., Karniadakis G., The Wiener-Askey polynomials chaos for stochastic Differential equations, Journal of Scientific Computing 26 Volume 24 Issue 2, 2002, 619 - 644.10.1137/S1064827501387826Search in Google Scholar

[26] Petras K., Smolyak cubature of given polynomial degree with few nodes for increasing dimension, Numerische Mathematik, 2002, Volume 93, Number 4, Pages 729-75310.1007/s002110200401Search in Google Scholar

[27] Baudin M., Martinez J., Polynômes de chaos sous Scilab via librairie NISP, in: 42 emes Journees de Statistique, 2010.Search in Google Scholar

[28] Heiss F., Winschel V., Likelihood approximation by numerical integration on sparse grids, Journal of Econometrics, Volume 144, Issue 1, May 2008, Pages 62-80 10.1016/j.jeconom.2007.12.004Search in Google Scholar

• Influence of the Numerical Modelling Methods on Dispersions and Errors of Analysis Results of Ship Hull and Deckhouse Vibrations
• Selected aspects of a movable shooting system of the Mi-24 helicopter
• Simulation of Current Distribution Through Elements of the Overhead Contact Line
• Technology of Making Airfield Pavements in Relation to the Existing Requirements in Terms of Texture
• An Application of the Pythagorean Fuzzy Sets in the Fault Diagnosis
• Analysis and Evaluation of the FA-50 Capabilities for the Polish Air Force
• Identification of Geological and Engineering Conditions for Forging the Bed of a Large Lowland River, Based on Low-Ceiling Ad Hoc Aerial Photographs
• Statistical Procedures for State Degradation Testing by Vibration Methods
• Preliminary Studies on the Purification of Bleaching Earth from Slack Wax Residues in the Adsorption Refining by Low-Temperature Pyrolysis Process
• The Process of Acquiring, Collecting, Processing and Archiving Data for the SHM System Designed to Identify Defects in Thin-Walled Structures
• The Peculiarities of Applying CAD/CAE Systems for the Primary Combustion Chamber Flame Tube Cycle Life Prolongation of the Tactical Military Aircraft Afterburning Turbofan Jet Engine
• Ground power unit LUZES as an experimental platform for studying defects for the FAM-C method
• Computer Aided Design Process for Hydraulic Servo
• Pilots’ Gaze Behavior in Simulation Research
• Actions of the aviation on-board fire protection system caused by short circuits in control blocks