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Reliability concept under general uncertainty

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Języki publikacji
EN
Abstrakty
EN
The Toyota crisis is tearing off the brand image of quality and reliability and therefore it is logical to question whether the dominating position of probability theory, on which Japanese quality and reliability engineering practices are established, should be examined. In general, reliability analysis is an exercise under uncertain environment. Foundationally speaking, uncertain modeling is a matter of choosing what kind of uncertain measure as its standing point. In this paper, we introduce the uncertainty reliability concept on the platform of the axiomatic uncertain measure theory and compare it to probabilistic reliability concept based on Kolmogorov’s probability measure theory, on which the traditional quality and reliability engineering is established. It is expecting that a foundational work can be established for a more rigorous reliability engineering and risk analysis under general uncertainty environments.
Rocznik
Strony
105--114
Opis fizyczny
Bibliogr. 19 poz., tab.
Twórcy
autor
  • University of Cape Town, Cape Town, South Africa
autor
  • University of Cape Town, Cape Town, South Africa
autor
  • University of Cape Town, Cape Town, South Africa
autor
  • South African National Biodiversity Institute, Cape Town, South Africa
Bibliografia
  • [1] Ash, R. B. (1972). Real Analysis and Probability. Academic Press.
  • [2] Carvalho, H. & Machado, V. C. (2006). Fuzzy set theory to establish resilient production systems. Proc. of IIE Annual Conference and Exhibition.
  • [3] Chung, K. L. (2001). A Course in Probability Theory. Third Edition.
  • [4] Guo, R. & Love, C.E. (1992). Statistical Analysis of An Age Model for Imperfectly Repaired System. Quality and Reliability Engineering International 8, 133-146.
  • [5] Guo, R. & Love, C. E. (1994). Simulating Nonhomogeneous Poisson Processes with Proportional Intensities. Naval Research Logistics, 41, 507-522.
  • [6] Guo, R., & Love, C. E. (2004). Fuzzy Covariate Modelling an Imperfectly Repaired System. Quality Assurance 10, 37, 7-15.
  • [7] Guo, R., Zhao, R. Q., Guo, D. & Dunne, T. (2007). Random Fuzzy Variable Modeling on Repairable System. Journal of Uncertain Systems 1, 3, 222-234, August.
  • [8] Guo, R., & Guo, D. (2009). Statistical Simulating Fuzzy Variable. Proceedings of the Eighth International Conference on Information and Management Sciences, Kunming & Banna, China, July 20-28, pp. 775-778. Editors: H. F. Wang, M. B. Neace, Y. G. Zhu, and W. Duch. ISSN 1539-2023.
  • [9] Kolmogorov, A. N. (1950). Foundations of the Theory of Probability. Translated by Nathan Morrison. Chelsea, New York.
  • [10] Lawless, J. F. (1982). Statistical Models and Methods for Lifetime Data. Wiley, NY .
  • [11] Liu, B. D. (2007). Uncertainty Theory: An Introduction to Its Axiomatic Foundations. Second Edition. Springer-Verlag Heidelberg, Berlin.
  • [12] Liu, B. D. (2009). Some research problems in uncertainty theory. Journal of Uncertain Systems 3, 1, 3-10.
  • [13] Liu, B. D. (2010). Uncertainty Theory. Third Edition (Drafted version).
  • [14] Love, C. E. & Guo, R. (1991). Using Proportional Hazard Modelling in Plant Maintenance. Quality and Reliability Engineering International 7, 7-17.
  • [15] Love, C. E. & Guo, R. (1991). Application of Weibull Proportional Hazards Modelling to Bad-As-Old Failure Data.. Quality and Reliability Engineering International 7: 149-157.
  • [16] Peng, Z. X. & Iwamura, K. (2007). A sufficient and necessary condition of uncertainty distribution. http://orsc.edu.cn/online/090305.pdf.
  • [17] Primas, H. (1999). Basic Elements and Problems of Probability Theory. Journal of Scientific Exploration 13, 4, 579-613.
  • [18] Zadeh, L. A. (1965). Fuzzy sets. Information and Control 8, 338-353.
  • [19] Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1, 3-28. (Reprint version: Vol.100, Supplement 1, 199, pp. 9-34).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-56a21d63-5a3b-49cc-a7ee-f8543bf7e9d3
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