Posbist fault tree analysis of coherent systems

Abstract When the failure probability of a system is extremely small or necessary statistical data from the system is scarce, it is very difficult or impossible to evaluate its reliability and safety with conventional fault tree analysis (FTA) techniques. New techniques are needed to predict and diagnose such a system's failures and evaluate its reliability and safety. In this paper, we first provide a concise overview of FTA. Then, based on the posbist reliability theory, event failure behavior is characterized in the context of possibility measures and the structure function of the posbist fault tree of a coherent system is defined. In addition, we define the AND operator and the OR operator based on the minimal cut of a posbist fault tree. Finally, a model of posbist fault tree analysis (posbist FTA) of coherent systems is presented. The use of the model for quantitative analysis is demonstrated with a real-life safety system.

[1]  WU Meng-da The Profust Fault Tree and its Quantity Analysis , 2001 .

[2]  Kai-Yuan Cai,et al.  System failure engineering and fuzzy methodology An introductory overview , 1996, Fuzzy Sets Syst..

[3]  Hong-Zhong Huang,et al.  Fuzzy Fault Tree Analysis of Railway Traffic Safety , 2000 .

[4]  B. Arzenšek,et al.  Failure of crane wire rope , 2002 .

[5]  Krishna B. Misra,et al.  Multi State Fault Tree Analysis Using Fuzzy Probability Vectors and Resolution Identity , 1995 .

[6]  Lev V. Utkin,et al.  A general formal approach for fuzzy reliability analysis in the possibility context , 1996, Fuzzy Sets Syst..

[7]  Cai Kaiyuan,et al.  Posbist reliability behavior of fault-tolerant systems , 1995 .

[8]  H. Trussell,et al.  Constructing membership functions using statistical data , 1986 .

[9]  Christian Cremona,et al.  The possibilistic reliability theory: theoretical aspects and applications , 1997 .

[10]  V. Venkat Raj,et al.  Uncertainty in fault tree analysis: A fuzzy approach , 1996, Fuzzy Sets Syst..

[11]  N. Shiraishi,et al.  Fuzzy importance in fault tree analysis , 1984 .

[12]  T. Onisawa An approach to human reliability on man-machine systems using error possibility , 1988 .

[13]  Swarup Medasani,et al.  An overview of membership function generation techniques for pattern recognition , 1998, Int. J. Approx. Reason..

[14]  L. Zadeh Fuzzy sets as a basis for a theory of possibility , 1999 .

[15]  Cai Kaiyuan,et al.  Fuzzy variables as a basis for a theory of fuzzy reliability in the possibility context , 1991 .

[16]  George J. Klir,et al.  A principle of uncertainty and information invariance , 1990 .

[17]  Etienne Kerre,et al.  On a possibilistic approach to reliability theory , 1993, 1993 (2nd) International Symposium on Uncertainty Modeling and Analysis.

[18]  K P Soman,et al.  Fuzzy Fault Tree Analysis using Resolution Identity , 1993 .

[19]  D. Singer A fuzzy set approach to fault tree and reliability analysis , 1990 .

[20]  Richard E. Barlow,et al.  Statistical Theory of Reliability and Life Testing: Probability Models , 1976 .

[21]  W. Yun,et al.  Fault tree analysis with fuzzy gates , 1997 .

[22]  James R. Wilson,et al.  An efficient and flexible mechanism for constructing membership functions , 2002, Eur. J. Oper. Res..

[23]  D. Dubois,et al.  Unfair coins and necessity measures: Towards a possibilistic interpretation of histograms , 1983 .

[24]  Kai-Yuan Cai,et al.  Fuzzy states as a basis for a theory of fuzzy reliability , 1993 .

[25]  Mao-Jiun J. Wang,et al.  Hybrid fault tree analysis using fuzzy sets , 1997 .

[26]  Singiresu S Rao,et al.  Fault tree analysis of fuzzy mechanical systems , 1994 .

[27]  Hideo Tanaka,et al.  Fault-Tree Analysis by Fuzzy Probability , 1983 .