Risk and reliability evaluation of gas connector systems using fuzzy theory and expert elicitation

References

• Ahmadi-Nedushan, B., St-Hilaire, A., Bérubé, M., Ouarda, T., & Robichaud, É. (2008). Instream flow determination using a multiple input fuzzy-based rule system: A case study. River Research and Applications, 24, 279–292.10.1002/(ISSN)1535-1467
   Web of Science ®Google Scholar
• Barker, B. (2014). Corrugated stainless steel tubing & flexible appliance connectors. Retrieved June 4, 2016 from http://www.ashireporter.org/HomeInspection/Articles/Corrugated-Stainless-Steel-Tubing-Flexible-Appliance-Connectors/13613
   Google Scholar
• Brito, A. J., & de Almeida, A. T. (2009). Multi-attribute risk assessment for risk ranking of natural gas pipelines. Reliability Engineering & System Safety, 94, 187–198.10.1016/j.ress.2008.02.014
   Web of Science ®Google Scholar
• Chanda, R. S., & Bhattacharjee, P. K. (1998). A reliability approach to transmission expansion planning using fuzzy fault tree model. Electric Power Systems Research, 45, 101–108.10.1016/S0378-7796(97)01226-1
   Web of Science ®Google Scholar
• Chen, Sh. J., & Hwang, Ch. L. (1990). Fuzzy multiple attribute decision making methods and applications (pp. 138–150). Berlin: Springer.
   Google Scholar
• Cheng, C. H., & Mon, D. L. (1993). Fuzzy system reliability analysis by interval of confidence. Fuzzy Sets and Systems, 56, 29–35.10.1016/0165-0114(93)90182-H
   Web of Science ®Google Scholar
• Dedianous, V., & Fiévez, C. (2006). ARAMIS project: A more explicit demonstration of risk control through the use of bow–tie diagrams and the evaluation of safety barrier performance. Journal of Hazardous Materials, 130, 220–233.10.1016/j.jhazmat.2005.07.010
   PubMed Web of Science ®Google Scholar
• Duijm, N. J. (2009). Safety-barrier diagrams as a safety management tool. Reliability Engineering & System Safety, 94, 332–341.10.1016/j.ress.2008.03.031
   Web of Science ®Google Scholar
• Eierdanz, F., Alcamo, J., Acosta-Michlik, L., Krömker, D., & Tänzler, D. (2008). Using fuzzy set theory to address the uncertainty of susceptibility to drought. Regional Environmental Change, 8, 197–205.10.1007/s10113-008-0069-1
   Web of Science ®Google Scholar
• 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 Analysis, 31, 86–107.10.1111/j.1539-6924.2010.01475.x
   PubMed Web of Science ®Google Scholar
• Furuta, H., & Shiraishi, N. (1984). Fuzzy importance in fault tree analysis. Fuzzy Sets and Systems, 12, 205–213.10.1016/0165-0114(84)90068-X
   Web of Science ®Google Scholar
• Guimarees, A. C., & Ebecken, N. (1999). Fuzzy FTA: A fuzzy fault tree system for uncertainty analysis. Annals of Nuclear Energy, 26, 523–532.10.1016/S0306-4549(98)00070-X
   Web of Science ®Google Scholar
• Huang, D., Chen, T., & Wang, J. M. (2001). A fuzzy set approach for event tree analysis. Fuzzy Sets and Systems, 118, 153–165.10.1016/S0165-0114(98)00288-7
   Web of Science ®Google Scholar
• Huang, H. Z., Tong, X., & Zuo, M. J. (2004). Posbist fault tree analysis of coherent systems. Reliability Engineering & System Safety, 84, 141–148.10.1016/j.ress.2003.11.002
   Web of Science ®Google Scholar
• IAEA. (2007). IAEA safety glossary, terminology used in nuclear safety and radiation protection. Vienna: Author.
   Google Scholar
• Jo, Y. D., & Ahn, B. J. (2002). Analysis of hazard areas associated with high-pressure natural-gas pipelines. Journal of Loss Prevention in the Process Industries, 15, 179–188.10.1016/S0950-4230(02)00007-4
   Web of Science ®Google Scholar
• Khakzad, N., Khan, F., & Amyotte, P. (2012). Dynamic risk analysis using bow-tie approach. Reliability Engineering & System Safety, 104, 36–44.10.1016/j.ress.2012.04.003
   Web of Science ®Google Scholar
• Khakzad, N., Khan, F., & Paltrinieri, N. (2014). On the application of near accident data to risk analysis of major accidents. Reliability Engineering & System Safety, 126, 116–125.10.1016/j.ress.2014.01.015
   Web of Science ®Google Scholar
• Khan, F. (2001). Use of maximum credible accident scenarios for realistic and reliable risk assessment. Chemical Engineering Progress, 11, 56–64.
   Google Scholar
• Knol, A. B., Slottje, P., van der Sluijs, J. P., & Lebret, E. (2010). The use of expert elicitation in environmental health impact assessment: A seven step procedure. Environmental Health, 9(19). doi:10.1186/1476-069X-9-19
   PubMed Web of Science ®Google Scholar
• Krueger, T., Page, T., Hubacek, K., Smith, L., & Hiscock, K. (2012). The role of expert opinion in environmental modelling. Environmental Modelling & Software, 36, 4–18. doi:10.1016/j.envsoft.2012.01.011
   Web of Science ®Google Scholar
• Liang, G. S., & Wang, M. J. (1993). Fuzzy fault-tree analysis using failure possibility. Microelectronics Reliability, 33, 583–597.10.1016/0026-2714(93)90326-T
   Web of Science ®Google Scholar
• Liao, K. X., Yao, A. L., & Zhang, H. X. (2001). Fault tree analysis of pipelines. Oil and Gas Transmission, 20, 27–30.
   Google Scholar
• Mamdani, E. H. (1977). Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions on Computers, C-26, 1182–1191.10.1109/TC.1977.1674779
   Web of Science ®Google Scholar
• Markowski, A. S., Mannan, M. S., & Bigoszewska, A. (2009). Fuzzy logic for process safety analysis. Journal of Loss Prevention in the Process Industries, 22, 695–702.10.1016/j.jlp.2008.11.011
   Web of Science ®Google Scholar
• Misra, K. B., & Weber, G. G. (1990). Use of fuzzy set theory for level-I studies in probabilistic risk assessment. Fuzzy Sets and Systems, 37, 139–160.10.1016/0165-0114(90)90038-8
   Web of Science ®Google Scholar
• Olawoyin, R., & McGlothlin, C. W. (2015). Explorative history of fuel gas connectors’ development and related regulatory standards. Journal of Natural Gas Science and Engineering, 27, 1470–1481.10.1016/j.jngse.2015.10.014
   Web of Science ®Google Scholar
• Onisawa, T. (1990). An application of fuzzy concepts to modelling of reliability analysis. Fuzzy Sets and Systems, 37, 267–286.10.1016/0165-0114(90)90026-3
   Web of Science ®Google Scholar
• Oster, H. J. (1998). Gas connectors: An explosive problem. Fire Engineering, 151, 101–106.
   Google Scholar
• Ping, H., Zhang, H., & Zuo, M. J. (2007). Fault tree analysis based on fuzzy logic. Annual Reliability and Maintainability Symposium, 10, 77–82.
   Google Scholar
• 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 and Systems, 243, 50–69.
   Web of Science ®Google Scholar
• Rai, V. (2013). Expert elicitation methods for studying technological change under uncertainty. Environmental Research Letters, 8, 041003.
   Web of Science ®Google Scholar
• Rajakarunakaran, S., Kumar, A. M., & Prabhu, V. A. (2015). Applications of fuzzy faulty tree analysis and expert elicitation for evaluation of risks in LPG refueling station. Journal of Loss Prevention in the Process Industries, 33, 109–123.10.1016/j.jlp.2014.11.016
   Web of Science ®Google Scholar
• Rathnayaka, S., Khan, F., & Amyotte, P. (2013). Accident modeling and risk assessment framework for safety critical decision-making: Application to deep-water drilling operation. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 227, 86–105.
   Web of Science ®Google Scholar
• Sadiq, R., Saint-Martin, E., & Kleiner, Y. (2008). Predicting risk of water quality failures in distribution networks under uncertainties using fault-tree analysis. Urban Water Journal, 5, 287–304.10.1080/15730620802213504
   Web of Science ®Google Scholar
• Shahriar, A., Sadiq, R., & Tesfamariam, S. (2012). Risk analysis for oil & gas pipelines: A sustainability assessment approach using fuzzy based bow-tie analysis. Journal of Loss Prevention in the Process Industries, 25, 505–523.10.1016/j.jlp.2011.12.007
   Web of Science ®Google Scholar
• Shalev, D. M., & Tiran, J. (2007). Conditioned-based fault tree analysis (CBTFA): A new method for improved fault tree analysis (FTA), reliability and safety calculations. Reliability Engineering & System Safety, 92, 1231–1241.10.1016/j.ress.2006.05.015
   Web of Science ®Google Scholar
• Spouge, J. (1999). A guide to quantitative risk assessment for offshore installations. Centre for Marine and Petroleum Technology CMPT, Publication 99/100a, DNV Technica. ISBN I 870553 365.
   Google Scholar
• Suresh, P. V., Babar, A. K., & Raj, V. V. (1996). Uncertainty in fault tree analysis: A fuzzy approach. Fuzzy Sets and Systems, 83, 135–141.10.1016/0165-0114(95)00386-X
   Web of Science ®Google Scholar
• Tanaka, H., Fan, L., Lai, F. S., & Toguchi, K. (1983). Fault-tree analysis by fuzzy probability. IEEE Transactions on Reliability, R-32, 453–457.10.1109/TR.1983.5221727
   Web of Science ®Google Scholar
• Tesfamaraim, S., & Saatcioglu, M. (2008). Seismic risk assessment of RC buildings using fuzzy synthetic evaluation. Journal of Earthquake Engineering, 12, 1157–1184.10.1080/13632460802003785
   Web of Science ®Google Scholar
• van der Sluijs, J. P., Craye, M., Funtowicz, S., Kloprogge, P., Ravetz, J., & Risbey, J. (2005). Combining quantitative and qualitative measures of uncertainty in model-based environmental assessment: The NUSAP system. Risk Analysis, 25, 2005.
   Web of Science ®Google Scholar
• Yuhua, D., & Datao, Y. (2005). Estimation of failure probability of oil and gas transmission pipelines by fuzzy fault tree analysis. Journal of Loss Prevention in the Process Industries, 18, 83–88.10.1016/j.jlp.2004.12.003
   Web of Science ®Google Scholar
• Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.10.1016/S0019-9958(65)90241-X
   Web of Science ®Google Scholar
• Zhang, G. D., & Lu, Y. X. (1990). Analysis and design of reliability and maintenance of system (pp. 120–125). Beijing: Beijing Aeronautics and Astronautics University Press.
   Google Scholar