Estimation of the importance measures of multi-state elements by Monte Carlo simulation

A generalization of some frequently used importance measures has been proposed by some of the authors for application to multi-state systems constituted by multi-state elements. This paper deals with the Monte Carlo (MC) estimation of these measures, which entails evaluating the system output performance under restrictions on the performance levels of its multi-state elements. Simulation procedures are proposed according to two different performance-restriction approaches. Further, the flexibility of the MC method is exploited to account for load-sharing and operational dependencies among parallel elements. The approach is tested on a multi-state transmission system of literature.

[1]  D. Vasseur,et al.  International survey on PSA figures of merit , 1999 .

[2]  A. Gandini,et al.  Importance and sensitivity analysis in assessing system reliability , 1990 .

[3]  Michael J. Armstrong Reliability -Importance and Dual Failure-Mode Com , 1997 .

[4]  Luca Podofillini,et al.  Generalised importance measures for multi-state elements based on performance level restrictions , 2003, Reliab. Eng. Syst. Saf..

[5]  Enrico Zio,et al.  Basics of the Monte Carlo Method with Application to System Reliability , 2002 .

[6]  Shaomin Wu,et al.  Performance utility-analysis of multi-state systems , 2003, IEEE Trans. Reliab..

[7]  Luca Podofillini,et al.  Importance Measures of Multi-State Components in Multi-State Systems , 2003 .

[8]  Michael J. Armstrong Joint reliability-importance of components , 1995 .

[9]  S. Garribba,et al.  Multiple-Valued Logic Trees: Meaning and Prime Implicants , 1985, IEEE Transactions on Reliability.

[10]  Alan P. Wood,et al.  Multistate Block Diagrams and Fault Trees , 1985, IEEE Transactions on Reliability.

[11]  Emanuele Borgonovo,et al.  A new importance measure for risk-informed decision making , 2001, Reliab. Eng. Syst. Saf..

[12]  Gregory Levitin,et al.  Importance and sensitivity analysis of multi-state systems using the universal generating function method , 1999 .

[13]  Elsayed A. Elsayed,et al.  Reliability Engineering , 1996 .

[14]  T. Aven On performance measures for multistate monotone systems , 1993 .

[15]  M. Vangel System Reliability Theory: Models and Statistical Methods , 1996 .

[16]  F. C. Meng Comparing the importance of system components by some structural characteristics , 1996, IEEE Trans. Reliab..

[17]  Michael J. Armstrong Reliability-importance and dual failure-mode components , 1997 .

[18]  Fan C. Meng Some further results on ranking the importance of system components , 1995 .

[19]  Gregory Levitin,et al.  Multi-State System Reliability - Assessment, Optimization and Applications , 2003, Series on Quality, Reliability and Engineering Statistics.

[20]  W. Griffith MULTISTATE RELIABILITY MODELS , 1980 .

[21]  David J. Sherwin,et al.  System Reliability Theory—Models and Statistical Methods , 1995 .

[22]  C. Lie,et al.  Joint reliability-importance of two edges in an undirected network , 1993 .

[23]  M. Cheok,et al.  Response to ‘Supplemental viewpoints on the use of importance measures in risk-informed regulatory applications’ , 1998 .

[24]  Laurence A. Baxter,et al.  Reliability importance for continuum structure functions , 1987 .

[25]  M. Cheok,et al.  Use of importance measures in risk-informed regulatory applications , 1998 .

[26]  M. van der Borst,et al.  An overview of PSA importance measures , 2001, Reliab. Eng. Syst. Saf..

[27]  Enrico Zio,et al.  Nonlinear Monte Carlo reliability analysis with biasing towards top event , 1993 .

[28]  J. B. Fussell HowtoHand-Calculate System Reliability andSafety Characteristics , 1975 .

[29]  Luca Podofillini,et al.  Monte Carlo simulation analysis of the effects of different system performance levels on the importance of multi-state components , 2003, Reliab. Eng. Syst. Saf..

[30]  F. C. Meng,et al.  Component-relevancy and characterization results in multistate systems , 1993 .