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The reliability of general vague fault-tree analysis on weapon systems fault diagnosis

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Abstract

An algorithm of vague fault-tree analysis is proposed in this paper to calculate fault interval of system components from integrating expert's knowledge and experience in terms of providing the possibility of failure of bottom events. We also modify Tanaka et al's definition and extend the new usage on vague fault-tree analysis in terms of finding most important basic system component for managerial decision-making. In numerical verification, the fault of automatic gun is presented as a numerical example. For advanced experiment, a fault tree for the reactor protective system is adopted as simulation example and we compare the results with other methods. This paper also develops vague fault tree decision support systems (VFTDSS) to generate fault-tree, fault-tree nodes, then directly compute the vague fault-tree interval, traditional reliability, and vague reliability interval.

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References

  1. Alexandre E, Sylviane G, Jacky M (2000) Fuzzy reasoning in co-operative supervision systems. Control Eng Pract 8:389–407

    Google Scholar 

  2. Cai KY (1996) System failure and fuzzy methodology: an introductory overview. Fuzzy Sets Syst 83:113–133

    Google Scholar 

  3. Chen SM (1995) Measures of similarity between vague sets. Fuzzy Sets Syst 74:217–223

    Google Scholar 

  4. Chen SM (2003) Analyzing fuzzy system reliability using vague sets theory. Int J Appl Sci Eng 1:82–88

    Google Scholar 

  5. Cheng CH, Mon DL (1993) Fuzzy system reliability analysis by confidence interval. Fuzzy Sets Syst 56:29–35

    Google Scholar 

  6. Eisenack K, Kropp J (2001) Assessment of management options in marine fisheries by qualitative modeling techniques. Mar Pollut Bull 43(7–12): 215–224

    Google Scholar 

  7. Furuta H, Shiraishi N (1984) Fuzzy importance in fault tree analysis. Fuzzy Sets Syst 12:205–213

    Google Scholar 

  8. Gau WL, Buehrer DJ (1993) Vague sets. IEEE Trans Syst Man Cybern 23:610–614

    Google Scholar 

  9. Goul M, Shane B, Tonge F (1986) Knowledge-based decision support system in strategic planning decision: an empirical Study. J Manage Inf Syst 2:70–84

    Google Scholar 

  10. Huang HZ, Tong X, Zuo MJ (2004) Posbist fault tree analysis of coherent systems. Reliability Eng Syst Safety 84:141–148

    Google Scholar 

  11. Kales P (1998) Reliability: for technology, engineering, and management. Prentice-Hall, Englewood Cliffs

  12. Kwok RCW, Ma J, Zhou DN (2002) Improving group decision making: a fuzzy GSS approach. IEEE Trans Syst Man Cybern SMC-32:54–63

    Google Scholar 

  13. Lee F (1998) Fuzzy information processing system. Peking University Press, pp 118–132

  14. Lee C, Lu TC, Lee NP, Chung UK (1999) The study of strategy on new equipment maintenance. Fuzzy Sets Math 13:37–44

    Google Scholar 

  15. Li SL (2000) The development of a hybrid intelligent system for developing marketing strategy. Decis Support Syst 27:395–409

    Google Scholar 

  16. Liang GS, Wang MJJ (1993) Fuzzy fault tree analysis using failure possibility. Microelectron Reliability 33:587–597

    Google Scholar 

  17. Liao SH (2001) A knowledge-based architecture for implementing military geographical intelligence system on Intranet. Expert Syst Appl 20:313–324

    Google Scholar 

  18. Lin CT, Wang MJ (1997) Hybrid fault tree analysis using fuzzy sets. Reliability Eng Syst Saf 58:205–213

    Google Scholar 

  19. Love E, Mats D, Magnus B (1997) Imposing security constraints on agent-based decision support. Decis Support Syst 20:3–15

    Google Scholar 

  20. Mikhailov L, Singh MG (2003) Fuzzy analytic network process and its application to the development of decision support systems. IEEE Trans Syst Man Cybern SMC-33:33–41

    Google Scholar 

  21. Mon DL, Cheng CH (1994) Fuzzy system reliability analysis for components with different membership functions. Fuzzy Sets Syst 64:145–157

    Google Scholar 

  22. Mon DL, Cheng CH (1993) Fuzzy system reliability analysis by interval of confidence. Fuzzy Sets Syst 56:29–35

    Google Scholar 

  23. Muller K, Sebastian HJ (1997) Intelligent systems for engineering design and configuration problems. Eur J Operational Res 100:315–326

    Google Scholar 

  24. Scott MS (1971) Management decision systems: computer support for decision making. Harvard University Press, Cambridge

  25. Singer D (1990) A fuzzy set approach to fault tree and reliability analysis. Fuzzy Sets Syst 34:145–155

    Google Scholar 

  26. Suresh PV, Babar AK, Raj VV (1996) Uncertainty in fault tree analysis: a fuzzy approach. Fuzzy Sets Syst 83:135–141

    Google Scholar 

  27. Tanaka H, Fan LT, Lai FS, Toguchi K (1983) Fault-tree analysis by fuzzy probability. IEEE Trans Reliability 32:150–163

    Google Scholar 

  28. Tam CM, Thomas KL, Gerald CW, Ivan WH (2002) Non-structural fuzzy decision support system for evaluation of construction safety management system. Int J Project Manage 20:303–313

    Google Scholar 

  29. Toshiyuki Y (1997) On a support system for human decision making by the combination of fuzzy reasoning and fuzzy structural modeling. Fuzzy Sets Syst 87:257–263

    Google Scholar 

  30. Walley P (1991) Statistical reasoning with imprecise probabilities. Chapman Hall, London

  31. Walley P (1997) Statistical inferences based on second-order possibility distribution. Int J Gen Syst 26:337–383

    Google Scholar 

  32. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Google Scholar 

  33. Borland C++ Builder, Borland Software Corporation

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Authors and Affiliations

  1. Department of Information Managements, NationalYunlin University of Science and Technology, 123 University Road, section 3, Touliu, Yunlin, 640, Taiwan

    J.-R. Chang & C.-H. Cheng

  2. Graduate School of Management, NationalYunlin University of Science and Technology, 123 University Road, section 3, Touliu, Yunlin, 640, Taiwan

    J.-R. Chang & C.-H. Cheng

  3. Graduate School of Resources Management, National Defense Management College, Chung-Ho, Taipei, Taiwan

    K.-H. Chang & S.-H. Liao

Authors
  1. J.-R. Chang
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  2. K.-H. Chang
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  3. S.-H. Liao
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  4. C.-H. Cheng
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Correspondence to J.-R. Chang.

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Chang, JR., Chang, KH., Liao, SH. et al. The reliability of general vague fault-tree analysis on weapon systems fault diagnosis. Soft Comput 10, 531–542 (2006). https://doi.org/10.1007/s00500-005-0483-y

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