Tackling uncertainty in safety risk analysis in process systems: The case of gas pressure reduction stations

Document Type : IIEC 2020

Authors

Department of industrial engineering, Yazd University, Yazd, Iran

Abstract

Industrial plants are subjected to very dangerous events. Therefore, it is very essential to carry out an efficient risk and safety analysis. In classical applications, risk analysis treats event probabilities as certain data, while there is much penurious knowledge and uncertainty in generic failure data that will lead to biased and inconsistent alternative estimates. Then, in order to achieve a better fitting with systems condition, uncertainty needs to be considered. One of the most usual analytical methods that have been widely applied in the field of risk analysis is the technic of failure mode and effects analysis (FMEA). The usage of this method is in identifying and abolishing the multiple failure modes in various phases of system, from the product design to production of the industries system operation. To solve the shortcomings in the traditional FMEA method, we propose an innovative approach consisted of Dempster Shafer evidence theory (DST) and FMEA to provide a more efficient way for failure mode identification and prioritization. The proposed methodology in this study can well capture imprecise opinions and can prioritize failure modes considering uncertainties. City Gate Station (CGS) of Yazd Province was used to prove the practical application and validity of the proposed risk analysis methodology. Results showed that the proposed method is effective and practical for real engineering purposes.

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Main Subjects

References

Atanassov, K. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96.
 
Chin, K. S., Wang, Y. M., Poon, G. K. K., & Yang, J. B. (2009). Failure mode and effects analysis using a group-based evidential reasoning approach. Computers & Operations Research, 36(6), 1768-1779.
 
Colli, M., Sala, R., Pirola, F., Pinto, R., Cavalieri, S., & Wæhrens, B. V. (2019). Implementing a dynamic FMECA in the digital transformation era. IFAC-PapersOnLine, 52(13), 755-760.
 
Dempster, A. (1967). Upper and lower probabilities induced by a mult-ivalued mapping. Annals of Mathematical Statistics, 38, 325-329.
 
Deng, Y. (2012). D numbers: theory and applications. Journal of Information &Computational Science, 9(9), 2421-2428.
 
Deng, X., Han, D., Dezert, J., Deng, Y., & Shyr, Y. (2015). Evidence combination from an evolutionary game theory perspective. IEEE transactions on cybernetics, 46(9), 2070-2082.
 
Ferdous, R., Khan, F., Sadiq, R., Amyotte, P., & Veitch, B. (2009). Handling data uncertainties in event tree analysis. Process safety and environmental protection, 87(5), 283-292.
 
He, R., Li, X., Chen, G., Wang, Y., Jiang, S., & Zhi, C. (2018). A quantitative risk analysis model considering uncertain information. Process Safety and Environmental Protection, 118, 361-370.
 
J. Guo, A risk assessment approach for failure mode and effects analysis based on intuitionistic fuzzy sets and evidence theory, J. Intell. Fuzzy Syst. 30 (2016)869–881.
 
Jiang, W. (2018). A correlation coefficient for belief functions. International Journal of Approximate Reasoning, 103, 94-106.
 
Jiang, W., Xie, C., Zhuang, M., & Tang, Y. (2017). Failure mode and effects analysis based on a novel fuzzy evidential method. Applied Soft Computing, 57, 672-683.
Kutlu, A. C., & Ekmekçioğlu, M. (2012). Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Systems with Applications, 39(1), 61-67.
 
Liu, L., Liu, H. C., & Lin, Q. L. (2011). An improved fmea using fuzzy evidential reasoning approach and grey theory. Fuzzy Systems and Mathematics, 2(25), 72-80.
 
Liu, H. C., You, J. X., Fan, X. J., & Lin, Q. L. (2014). Failure mode and effects analysis using D numbers and grey relational projection method. Expert Systems with Applications, 41(10), 4670-4679.
 
Liu, H. C. (2019). Improved FMEA methods for proactive healthcare risk analysis. Springer Singapore.
 
Li, X., Chen, G., Jiang, S., He, R., Xu, C., & Zhu, H. (2018). Developing a dynamic model for risk analysis under uncertainty: case of third-party damage on subsea pipelines. Journal of Loss Prevention in the Process Industries, 54, 289-302.
 
Li, Z., & Chen, L. (2019). A novel evidential FMEA method by integrating fuzzy belief structure and grey relational projection method. Engineering Applications of Artificial Intelligence, 77, 136-147.
 
Nourian, R., Mousavi, S. M., & Raissi, S. (2019). A fuzzy expert system for mitigation of risks and effective control of gas pressure reduction stations with a real application. Journal of Loss Prevention in the Process Industries, 59, 77-90.
 
Nourian, R., & Mousavi, S. M. (2019). Design and implementation of an expert system for periodic and emergency control under uncertainty: A case study of city gate stations. Journal of Natural Gas Science and Engineering, 66, 306-315.
 
Sentz, K., & Ferson, S. (2002). Combination of evidence in Dempster-Shafer theory (Vol. 4015). Albuquerque: Sandia National Laboratories.
 
Shi, C., Cheng, Y. M., & Pan, Q. (2012). Method to aggregate hybrid preference information based on intuitionistic fuzzy and evidence theory. Control and Decision, 27(8), 1163-1168.
 
Shishebori, D., Akhgari, M. J., Noorossana, R., & Khaleghi, G. H. (2015). An efficient integrated approach to reduce scraps of industrial manufacturing processes: a case study from gauge measurement tool production firm. The International Journal of Advanced Manufacturing Technology, 76(5-8), 831-855.
 
Shishebori, D., & Zeinal Hamadani, A. (2010). The effect of gauge measurement capability and dependency measure of process variables on the MCp. Journal of Industrial and Systems Engineering, 4(1), 59-76.
 
Si, X. S., Hu, C. H., Yang, J. B., & Zhang, Q. (2011). On the dynamic evidential reasoning algorithm for fault prediction. Expert Systems with Applications, 38(5), 5061-5080.
 
Tian, Z. P., Wang, J. Q., & Zhang, H. Y. (2018). An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods. Applied Soft Computing, 72, 636-646.
 
W. Jiang, C. Xie, B. Wei, D. Zhou, A modified method for risk evaluation in failure modes and effects analysis of aircraft turbine rotor blades, Adv. Mech.Eng. 8 (2016) 1–6.
 
Wang, Z., Gao, J. M., Wang, R. X., Chen, K., Gao, Z. Y., & Jiang, Y. (2018). Failure mode and effects analysis using Dempster-Shafer theory and TOPSIS method: Application to the gas insulated metal enclosed transmission line (GIL). Applied Soft Computing, 70, 633-647.
 
Wang, Z., Gao, J. M., Wang, R. X., Chen, K., Gao, Z. Y., & Jiang, Y. (2018). Failure mode and effects analysis using Dempster-Shafer theory and TOPSIS method: Application to the gas insulated metal enclosed transmission line (GIL). Applied Soft Computing, 70, 633-647.
 
Yager, R. R. (1987). On the Dempster-Shafer framework and new combination rules. Information sciences, 41(2), 93-137.
 
Yang, J., Huang, H. Z., He, L. P., Zhu, S. P., & Wen, D. (2011). Risk evaluation in failure mode and effects analysis of aircraft turbine rotor blades using Dempster–Shafer evidence theory under uncertainty. Engineering Failure Analysis, 18(8), 2084-2092.
 
Yang, J., Wen, D., Huang, H. Z., Wan, H., & Sun, R. (2011, June). Reliability analysis of aircraft servo-actuation systems based on the evidential networks with imprecise information. In 2011 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (pp. 187-194). IEEE.
 
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
 
Zadeh, L. A. (2011). A note on Z-numbers. Information Sciences, 181(14), 2923-2932.
 
Zhou, Q., & Thai, V. V. (2016). Fuzzy and grey theories in failure mode and effect analysis for tanker equipment failure prediction. Safety science, 83, 74-79.

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  • Receive Date: 09 August 2020
  • Accept Date: 09 August 2020

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