Applications of fuzzy faulty tree analysis and expert elicitation for evaluation of risks in LPG refuelling station (original) (raw)
Abstract
A method is presented for analysis of reliability of complex engineering systems using information from fault tree analysis and uncertainty/imprecision of data. Fuzzy logic is a mathematical tool to model inaccuracy and uncertainty of the real world and human thinking. The method can address subjective, qualitative, and quantitative uncertainties involving risk analysis. Risk analysis with all the inherent uncertainties is a prime candidate for Fuzzy Logic application. Fuzzy logic combined with expert elicitation is employed in order to deal with vagueness of the data, to effectively generate basic event failure probabilities without reliance on quantitative historical failure data through qualitative data processing. The proposed model is able to quantify the fault tree of LPG refuelling facility in the absence or existence of data. This paper also illustrates the use of importance measures in sensitivity analysis. The result demonstrates that the approach is an apposite for the probabilistic reliability approach when quantitative historical failure data are unavailable. The research results can help professionals to decide whether and where to take preventive or corrective actions and help informed decision-making in the risk management process.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.
References (41)
- Chanda, R.S., Bhattacharjee, P.K., 1998. A reliability approach to transmission expansion planning using fuzzy fault tree model. Electr. Power Syst. Res. 45, 101e108.
- Cheng, C.H., Mon, D.L., 1993. Fuzzy system reliability analysis by interval of confi- dence. Fuzzy Sets Syst. 56, 29e35.
- Dong, H.Y., Yu, D.U., 2005. Estimation of failure probability of oil and gas trans- mission pipelines by fuzzy fault tree analysis. J. Loss Prev. Process Ind. 18, 83e88.
- Ericson, C.A., 2005. Fault tree analysis. In: Ericson (Ed.), Hazard Analysis Techniques for System Safety. John Wiley & Sons, Virginia, pp. 183e221.
- Ferdous, R., Khan, F., Sadiq, R., Amyotte, P., Veitch, B., 2011. Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations. Risk Anal. 31, 86e107.
- Furuta, H., Shiraishi, N., 1984. Fuzzy importance in fault tree analysis. Fuzzy Sets Syst. 12, 205e213.
- Guimarees, A.C., Ebecken, N., 1999. Fuzzy FTA: a fuzzy fault tree system for un- certainty analysis. Ann. Nucl. Energy. 26, 523e532. Health and Safety Executive, 1978. Canvey: an Investigation of Potential Hazards from Operations in the Canvey Island/Thurrock Area ((the First Canvey Report)).
- Hryniewicz, O., 2007. Fuzzy sets in the evaluation of reliability. In: Levitin, G. (Ed.), Computational Intelligence in Reliability Engineering New Metaheuristics, Neural and Fuzzy Techniques in Reliability. Springer-Verlag, Berlin, Heidelberg, pp. 363e386.
- Hsu, H.M., Chen, T.C., 1994. Aggregation of fuzzy opinion under group decision making. Fuzzy Sets Syst. 79, 279e285.
- Huang, D., Chen, T., Wang, M.J.J., 2001. A fuzzy set approach for event tree analysis. Fuzzy Sets Syst. 118, 153e165.
- Huang, H.Z., Tonga, X., Zuo, M.J., 2004. Posbist fault tree analysis of coherent sys- tems. Reliab. Eng. Syst. Saf. 84, 141e148.
- IAEA, 2007. IAEA Safety Glossary, Terminology Used in Nuclear Safety and Radiation Protection. IAEA, Vienna, Austria.
- Karimi, I., Hüllermeier, E., 2007. Risk assessment system of natural hazards: a new approach based on fuzzy probability. Fuzzy Sets Syst. 158, 987e999.
- Klir, J.G., Yuan, B., 2001. Fuzzy Sets and Fuzzy Logic Theory and Applications. PrenticeeHall.
- Liang, G.S., Wang, M.J., 1993. Fuzzy fault-tree analysis using failure possibility. Microelectron. Reliab. 33, 583e597.
- Lin, S.W., Bier, V.M., 2008. A study of expert overconfidence. Reliab. Eng. Syst. Saf. 93, 711e721.
- Lin, T.C., Wang, M.J., 1997. Hybrid fault tree analysis using fuzzy sets. Reliab. Eng. Syst. Saf. 58, 205e231.
- Liu, J., Yang, J.B., Ruan, D., Martinez, L., Wang, J., 2008. Self-tuning of fuzzy belief rule bases for engineering system safety analysis. Ann. Oper. Res. 163, 143e168.
- Melchers, R.E., Feutrill, W.R., 2001. Risk assessment of LPG automotive refueling facilities. Reliab. Eng. Syst. Saf. 74, 283e290.
- Miller, G.A., 1956. The magical number seven plus or minus two: some limit on our capacity for processing information. Psychol. Rev. 63 (1), 81e97.
- Misra, K.B., Weber, G.G., 1990. Use of fuzzy set theory for level-studies in proba- bilistic risk assessment. Fuzzy Sets Syst. 37 (2), 139e160.
- Modarres, M., 2006. Risk Analysis in Engineering: Probabilistic Techniques, first ed. CRC Publishing, USA.
- Norris, J.R., 1998. Markov Chains, Statistical and Probabilistic Mathematics: Series 2, second ed. Cambridge university Press, UK.
- Onisawa, T., 1988. An approach to human reliability in man-machine systems using error possibility. Fuzzy Sets Syst. 27 (2), 87e103.
- Onisawa, T., 1990. An application of fuzzy concepts to modelling of reliability analysis. Fuzzy Sets Syst. 37 (6), 266e286.
- Onisawa, T., 1989. 'Fuzzy theory in reliability analysis'. Fuzzy Sets Syst. 30 (3), 361e363.
- Pan, N.F., Wang, H., 2007. Assessing failure of bridge construction using fuzzy fault tree analysis. In: IEEE International Conference on Fuzzy Systems and Knowl- edge Discovery, vol. 1, pp. 96e100. Haikou.
- Ping, H., Zhang, H., Zuo, M.J., 2007. Fault tree analysis based on fuzzy logic. Annu. Reliab. Maintainab. Symp. 10 (4), 77e82.
- Purba, J.H., Lu, Jie, Zhang, G., Pedrycz, W., 2013. A fuzzy reliability assessment of basic events of fault trees through qualitative data processing. Fuzzy Sets Syst. http://dx.doi.org/10.1016/j.fss.2013.06.009.
- Ross, T.J., 2004b. Development of membership functions. In: Fuzzy Logic with En- gineering Applications, second ed. John Wiley & Sons, West Sussex, England, pp. 178e211.
- Ross, T.J., 2004a. Properties of membership functions, fuzzifications, and defuzzi- fication. In: Fuzzy Logic with Engineering Applications, second ed. John Wiley & Sons, West Sussex, England, pp. 90e119.
- Shu, M.H., Cheng, C.H., Chang, J.R., 2006. Using intuitionistic fuzzy fault-tree anal- ysis on printed circuit board assembly. Microelectron. Reliab. 46, 2139e2146.
- Singer, D., 1990. A fuzzy set approach to fault tree and reliability analysis. Fuzzy Sets Syst. 34, 145e155.
- Sugeno, M., 1999. Fuzzy Modeling and Control, first ed. CRC Press, Florida, USA.
- Suresh, P.V., Babar, A.K., Venkat Raj, V., 1996. Uncertainty in fault tree analysis: a fuzzy approach. Fuzzy sets Syst. 83, 135e141.
- Tanaka, H., Fan, L., Lai, S., Toguchi, K., 1983. Fault tree analysis by fuzzy probability. IEEE Trans. Reliab. 32, 453e457.
- Tang, Z., Dugan, J.B., 2004. Minimal cutset/sequence generation for dynamic fault trees. In: Proceedings of the IEEE Annual Reliability and Maintainability Sym- posium, pp. 207e213.
- Ung, S.T., Shen, W.M., 2011. A novel human error probability assessment using fuzzy modeling. Risk Anal. 31, 745e757.
- Wolkenhauer, O., 2001. Fuzzy mathematics. In: Data Engineering: Fuzzy Mathe- matics in Systems Theory and Data Analysis. John Wiley & Sons, Inc, pp. 197e212.
- Yu, D., Park, W.S., 2000. Combination and evaluation of expert opinions charac- terized in terms of fuzzy probabilities. Ann. Nucl. Energy 27, 713e726.
- Zadeh, L.A., 1965. Fuzzy sets. Inf. Control 8, 338e353.