Journal of the Korea Safety Management & Science (대한안전경영과학회지)

Korea Safety Management & Science (대한안전경영과학회)
권호 표지

A Study Fuzzy model for Risk Analysis of Uncertainly FTA(Fault Tree Analysis)

FTA(Fault Tree Analysis)에서 불확실한 위험분석을 위한 퍼지모형 연구

  • 임총규 (명지대학교 산업공학과) ;
  • 박주식 (명지대학교 산업공학과) ;
  • 강경식 (명지대학교 산업공학과)
  • Published : 2002.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

Risk analysis is a formal deductive procedure for determining combinations of component failures and human errors that could result in the occurrence of specified undesired events at the system level. This method can be used to analyze the vast majority of industrial system reliability problems. This study deals with the application of knowledge-engineering and a methodology for the assessment ＆ measurement of reliability, availability, maintainability, and safety of industrial systems using FTA(fault tree analysis), A fuzzy methodology for fault-tree evaluation seems to be an alternative solution to overcome the drawbacks of the conventional approach (insufficient Information concerning the relative frequencies of hazard events). To improve the quality of results, the membership functions must be approximated based on heuristic considerations, The purpose of this study is to describe the knowledge engineering approach, directed to integrate the various sources of knowledge involved in a FTA.

Keywords

References

  1. 김길동, 조 암, '퍼지집합에 의한 FT분석 및 신뢰성 분석,' 산업안전학회지, Vol.12, No.3, 1997, pp.155-160
  2. Brown, C. B., 'A Fuzzy Safety Measure,' J. Eng. Mech. Div, Vol.5, 1979, pp.855-872
  3. Eshragh, F. and E. H. Mamdani, 'A General Approach to Linguistic Approximation,' Int. J. Man-Machine Stud., Vol.11, 1979, pp.501-519
  4. Fine, W. T., Mathematical Evaluations for Controlling Hazards., Academic Press, 1973
  5. Graham, K. T., G. T. Kinney, 'A Practical Safety Analysis System for Hazards Control', J. Safety Research, Vol.12, 1980, pp.13-20
  6. Karwoski, W., A. Mital, 'Application of approximate reasoning in risk analysis,' Applications of Fuzzy Set Theory in Human Factors, 1986, pp.227-243
  7. Kaufmann, A., Introduction Theroy of Fuzzy Subsets, Vol.I, Academic Press, New York, 1975
  8. Nath, A. K. and T. T. Lee, 'On the Design of a Classifier with Linguistic Variables as Inputs,' Fuzzy Sets and Systems, Vol.11, 1983, pp.265-286
  9. Wenstop, F., 'Quantitative Analysis with Linguistic Values,' Fuzzy Sets and Systems, Vol.4, 1980, pp.99-115
  10. Zadeh, L. A., 'A Fuzzy Algorithmic Approach to Definition of Complex and Imprecise Concepts,' Int. J. Man-Machine Stud, Vol.8, 1976, pp.249-291 https://doi.org/10.1016/S0020-7373(76)80001-6
  11. Zadeh, L. A., 'A Fuzzy Logic and Approximate Reasoning,' Synthese, Vol.30, 1975, pp.407-428
  12. Zadeh, L. A., 'Outline of a new approach th the analysis of complex systems and decision processws,' IEEE Trans. Systems Man Cybernet., Vol.3, 1973, pp.28-44
  13. Zadeh, L. A., 'The Concept of Linguistic Variable and Its Application to Approximate Reasoning,' Information Sci., Part I; Vol.8, pp.199-249, Part II; Vol.8, pp.301-357, Part Ill; Vol.9, 1975, pp.43-80
  14. Zadeh, L. A., 'The concept of a linguistic variable ant its application to approximate reasoning,' Information Science, Vol.8, 1975, pp.301-357
  15. Zimmer, A. C., 'A Model for the Interpretation of Verbal Predictions,' Int. J. Man-Machine Stud, Vol.20, 1984, pp.121-134
  16. Zimmer, A. C., Verbal versus Numerical Processing, North-Holland, Amsterdam, 1983