Approximate multi-state reliability expressions using a new machine learning technique

Reliability Engineering and System Safety

Volumn 89, Issue 3, 2005, Pages 261-270

  Rocco S , Claudio M ^a   Muselli, Marco ^b  

^a UNIVERSIDAD CENTRAL DE VENEZUELA   (Venezuela)

^b CNR   (Italy)

Author keywords

Hamming clustering; Multi state system; Network reliability evaluation; Reliability expression; Rule generation

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; BOUNDARY VALUE PROBLEMS; COMPUTATIONAL METHODS; FUNCTIONS; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; OPTIMIZATION; PROBLEM SOLVING;

HAMMING CLUSTERING; MULTI-STATE SYSTEM; NETWORK RELIABILITY EVALUATION; RELIABILITY EXPRESSION; RULE GENERATION;

RELIABILITY;

EID: 15844418259     PISSN: 09518320     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ress.2004.08.023     Document Type: Article

References (23)

1. C. Colbourn The combinatorics of network reliability 1987 Oxford University Press New York
2. K.K. Aggarwal, Y.C. Chopra, and J.S. Bajwa Capacity consideration in reliability analysis of communication systems IEEE Trans Reliab 31 2 1982
3. A. Lisnianski, and G. Levitin Multi-state system reliability 2003 World Scientific New Jersey
4. A. Rauzy, E. Châtelet, Y. Dutuit, and C. Bérenguer A practical comparison of methods to assess sum-of-products Reliab Eng Syst Saf 79 2003 33 42
5. E. Zio, L. Podofillini, and G. Levitin Estimation of the importance measures of multi-state elements by Monte Carlo simulation. Reliab Eng Syst Saf 86(3) 2004 179 337
6. J.E. Ramirez-Márquez, and D.W. Coit Monte-Carlo simulation approach for approximating multi-state two-terminal reliability Reliab Eng Syst Saf 87(2) 2005 149 297
7. Y. Lin A simple algorithm for reliability evaluation of a stochastic-flow network with node failure Comput Oper Res 28 2001 1277 1285
8. J.E. Ramirez-Marquez, and D. Coit Alternative approach for analyzing multistate network reliability Proceedings of the Industrial Engineering Research Conference, Portland, OR 2003
9. Y. Lin Using minimal cuts to evaluate the system reliability of a stochastic-flow network with failures at nodes and arcs Reliab Eng Syst Saf 75 2002 41 46
10. W. Yeh A simple MC-based algorithm for evaluating reliability of a stochastic-flow using minimal cuts to evaluate the system reliability of a stochastic-flow network with unreliable nodes Reliab Eng Syst Saf 2004 [in press]
11. Ramirez-Marquez JE, Coit D, Tortorella M. Multistate two-terminal reliability: a cut approach. Rutgers University IE working paper 03-135; 2003.
12. Ramirez-Marquez JE, Coit D, Tortorella M. Bounds for multistate network two-terminal reliability. Rutgers University IE working paper 03-121; 2003.
13. S.C.M. Rocco, and M. Muselli Empirical models based on machine learning techniques for determining approximate reliability expression Reliab Eng Syst Saf 83 2004 301 309
14. E. Reingold, J. Nievergelt, and N. Deo Combinatorial algorithms: theory and practice 1977 Prentice Hall New Jersey
15. C.H. Papadimitriou, and K. Steiglitz Combinatorial optimization: algorithms and complexity 1982 Prentice Hall New Jersey
16. M. Muselli, and D. Liberati Binary rule generation via Hamming clustering IEEE Trans Knowl Data Eng 14 2002 1258 1268
17. M. Muselli, and D. Liberati Training digital circuits with Hamming clustering IEEE Trans Circ Syst I. 47 2000 513 527
18. J.R. Quinlan C4.5: Programs for machine learning 1993 Morgan Kaufmann San Mateo, CA
19. K.D. Heidtmann Smaller sums of disjoint products by subproducts inversion IEEE Trans Reliab 38 1989 305 311
20. K. Veropoulos, C. Campbell, and N. Cristianini Controlling the sensitivity of support vector machines Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm, Sweden 1999 p. 55-60
21. Torgo LF. Inductive learning of tree-based regression models, PhD Thesis, Faculdade de Ciências da Universidade do Porto; 1999.
22. T. Jin, and D. Coit Network reliability estimates using linear and quadratic unreliability of minimal cuts Reliab Eng Syst Saf 82 2003 41 48
23. S.C.M. Rocco, and M. Muselli A machine learning algorithm to estimate minimal cut and path sets from a Monte Carlo simulation Proceedings Probabilistic Safety Assessment and Management PSAM7/ESREL'04 2004 Springer Berlin p. 3142-7