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New reliability score for component strength using kullback-leibler divergence

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Nowa metoda oceny niezawodności na podstawie wytrzymałości elementów z zastosowaniem dywergencji Kullbacka-Leiblera
Języki publikacji
EN
Abstrakty
EN
The reliability of technical systems is one of the most important research subjects in the point reached by modern science. In many recent studies, this problem is solved by evaluation the operation performance of determined one or more components operating under stress. At this point, R=P(X
PL
Niezawodność systemów technicznych jest jednym z najważniejszych tematów badawczych we współczesnej nauce. Wiele z ostatnich badań, problem ten rozwiązuje poprzez ocenę wydajności pracy jednego lub większej liczby wybranych elementów działających pod wpływem obciążenia. Za punkt wyjściowy przyjmuje się R=P(X
Rocznik
Strony
367--372
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
autor
  • Department of Statistics, Faculty of Science Fırat University, TR-23119 Elazığ, Turkey
autor
  • Department of Statistics, Faculty of Science Fırat University, TR-23119 Elazığ, Turkey
Bibliografia
  • 1. Andrzejczak K. Some properties of multistate BW-systems. Serdica Bulgaricae Mathematicae Publicationes 1987; 13: 341-346.
  • 2. Andrzejczak K. Deterministic properties of the multistate systems. Proceedings of the Fifth Anniversary International Conference RELCOMEX'89, Poland, Książ Castle, September. 1989; 25-32.
  • 3. Andrzejczak K. Structure analysis of multistate coherent systems. Optimization 1992; 25: 301-316, http://dx.doi.org/10.1080/02331939208843826.
  • 4. Awad A M, Gharraf M K. Estimating of P(Y
  • 5. Basu A P, Ebrahimi N. On the reliability of stochastic systems. Statist. Probab. Lett. 1983; 1: 265-267, http://dx.doi.org/10.1016/0167-7152(83)90042-1.
  • 6. Basu S, Lingham R T. Bayesian estimation of system reliability in Brownian stress-strength models. Ann. Inst. Statist. Math. 2003; 55: 7-19, http://dx.doi.org/10.1007/BF02530482.
  • 7. Brunelle R D, Kapur K C. Review and classification of reliability measures for multistate and continuum models. IIE Transactions 1999; 31:1171-1180, http://dx.doi.org/10.1080/07408179908969917.
  • 8. Chandra S, Owen D B. On estimating the reliability of a component subject to several different stresses (strengths). Naval Res. Log. Quart. 1975; 22: 31-40, http://dx.doi.org/10.1002/nav.3800220104.
  • 9. Dahlhaus R. On Kullback-Leibler information divergence of locally stationary processes. Stoch. Process. Appl. 1996; 62: 139-168, http://dx.doi.org/10.1016/0304-4149(95)00090-9.
  • 10. Do M N. Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models. IEEE Signal Processing Lett. 2003; 10: 115-118, http://dx.doi.org/10.1109/LSP.2003.809034.
  • 11. Ebrahimi N. Multistate reliability models. Naval Research Logistics Quarterly 1984; 31: 671-680, http://dx.doi.org/10.1002/nav.3800310415.
  • 12. Ebrahimi N. Two suggestions of how to define a stochastic stress-strength system. Statist. Probab. Lett. 1985; 3: 295-297, http://dx.doi.org/10.1016/0167-7152(85)90058-6.
  • 13. Ebrahimi N. Ramallingam T. Estimation of system reliability in Brownian stress-strength models based on sample paths. Ann. Inst. Statist. Math. 1993; 45: 9-19, http://dx.doi.org/10.1007/BF00773665.
  • 14. El-Neweihi E, Proschan F, Sethuraman J. Multi-state coherent system. Journal of Applied Probability 1978; 15: 675-688, http://dx.doi.org/10.2307/3213425
  • 15. Eryılmaz S. Mean Residual and Mean Past Lifetime of Multi-State Systems With Identical Components. IEEE Trans. Reliab. 2010; 59: 644-649, http://dx.doi.org/10.1109/TR.2010.2054173.
  • 16. Eryılmaz S, İşçioğlu F. Reliability evaluation for a multi-state system under stress-strength setup. Commun. Statist. Theor. Meth. 2011; 40:547-558, http://dx.doi.org/10.1080/03610920903411242.
  • 17. Gradshteyn I S, Ryzhik I M. Table of Integrals, Series and Products. 6th ed. California: Academic Press, 2000.
  • 18. Hall P. On Kullback-Leibler loss and density estimation. Ann. Statist. 1987; 15: 1491-1519, http://dx.doi.org/10.1214/aos/1176350606.
  • 19. Hudson J C, Kapur K C. Reliability analysis for multistate systems with multistate components. IIE Transactions 1983; 15: 127-135, http://dx.doi.org/10.1080/05695558308974623.
  • 20. Kotz S, Lumelskii Y, Pensky M. The Stress-Strength Model and its Generalizations. Theory and Applications. Singapore: World Scientific,2003, http://dx.doi.org/10.1142/9789812564511.
  • 21. Kullback S, Leibler R A. On information and sufficiency. Ann. Math. Statist. 1951; 22: 79-86, http://dx.doi.org/10.1214/aoms/1177729694.
  • 22. Kuo W, Zuo M J. Optimal Reliability Modeling, Principles and Applications. New York: John Wiley & Sons, 2003. 23, Lee Y K, Park B U. Estimation of Kullback-Leibler divergence by local likelihood. Ann. Inst. Statist. Math. 2006; 58: 327-340.
  • 23. Lee Y K, Park B U. Estimation of Kullback-Leibler divergence by local likelihood. Ann. Inst. Statist. Math. 2006; 58: 327-340.
  • 24. Nadarajah S, Kotz S. Reliability for some bivariate exponential distributions. Mathematical Problems in Engineering 2006; 2006: 1-14, http://dx.doi.org/10.1155/mpe/2006/41652.
  • 25. Rached Z, Alajaji F, Lorne Campbell L. The Kullback-Leibler Divergence Rate Between Markov Sources. IEEE Trans. Inform. Theory 2004; 50: 917-921, http://dx.doi.org/10.1109/TIT.2004.826687.
  • 26. Smith A, Naik P A, Tsai C L. Markov-switching model selection using Kullback-Leibler divergence. Journal of Econometrics 2006; 134: 553-577, http://dx.doi.org/10.1016/j.jeconom.2005.07.005.
  • 27. Wang Q, Kulkarni S, Verdú S. Divergence Estimation of Continuous Distribution based on data-dependent Partitions. IEEE Trans. Inform. Theory 2005; 51: 3064-3074, http://dx.doi.org/10.1109/TIT.2005.853314.
  • 28. Whitmore G A. On the reliability of stochastic systems: a comment. Statist. Probab. Lett. 1990; 10: 65-67, http://dx.doi.org/10.1016/0167-7152(90)90113-L.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3c09467d-23e3-4c60-a857-6e811d2c237d
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