FMEA based assessment of risk that metro train stops abnormally

doi:10.16265/j.cnki.issn1003-3033.2017.02.026

Abstract

Abstract: There are presently two problems in the safety risk assessment of subway trains operation systems, which are less consideration of the expert behavior preference and understanding partially the weights of risk factors.Secondly a method of risk assessment based on both PT and FMEA was worked out,consisting of four steps.The ranges of risk factor weights were determined by the expert team based on experience and historical data. A model was built by using the technique for order preference by similarity to ideal solution for the risk factors weight optimization. Thirdly, the evaluation matrix was transformed into the prospect value matrix by the prospect theory. Lastly, the PROMETHEE was used to sort the failure mode risks. The effectiveness and feasibility of the proposed method were verified by the case of assessment of risk that a metro train stops abnormally in a certain city. The results show that the top three of the non normal stopping modes of the metro train are the emergency, foreign body intrusion, people's interference, which conforms with the reality.

Key words: prospect theory(PT), failure modes and effect analysis(FMEA), non-normal parking, metro, risk assessment, preference ranking organization method for enrichment evaluations(PROMETHEE)

CLC Number:

ZHU Jianghong , LI Guofang , WANG Rui , LI Yanlai. FMEA based assessment of risk that metro train stops abnormally[J]. China Safety Science Journal, 2017, 27(2): 145-150.

References

[1] 肖雪梅,王艳辉,贾利民. 基于复杂网络和熵的城轨路网运营安全评价模型[J]. 中国安全科学学报,2011,21(11):41-48.
XIAO Xuemei, WANG Yanhui, JIA Limin. Safety assessment model for urban rail transit network operations based on complex network and entropy theory[J]. China Safety Science Journal, 2011,21(11):41-48.
[2] LIU Huchen, LIU Long, LIU Nan, et al. Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment[J]. Expert Systems With Applications,2012,39(17):12 926-12 934.
[3] GUO Jian. A risk assessment approach for failure mode and effects analysis based on intuitionistic fuzzy sets and evidence theory[J]. Journal of Intelligent & Fuzzy Systems,2016,30(2):869-881.
[4] VAHDANI Behnam, SHALIMI M, CHARKHCHIAN M. A new FMEA method by integrating fuzzy belief structure and TOPSIS to improve risk evaluation process[J]. International Journal of Advanced Manufacture Technology,2015,77(1/2/3/4):257-368.
[5] CHANG Kueihu, CHANG Yungchina, LEE Yutsai. Integrating TOPSIS and DEMATEL methods to rank the risk of failure of FMEA[J]. International Journal of Information Technology & Decision Making,2014,13(6):1 229-1 257.
[6] LIU Huchen, FAN Xiaojun, LI Ping, et al. Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment[J]. Engineering Applications of Artificial Intelligence,2014,34(C):168-177.
[7] LIU Huchen, YOU Jianxin, YOU Xiaoyue. Evaluating the risk of healthcare failure modes using interval 2-tuple hybrid weighted distance measure[J]. Computer & Industrial Engineering,2014,78:249-258.
[8] 耿秀丽,张永政. 基于犹豫模糊集的改进FMEA风险评估方法[J]. 计算机集成制造系统,(2016-06-03). http://www.cnki.net/kcms/detail/11.3619.TP. 20160603.1613.010.html.
GENG Xiuli, ZHANG Yongzheng. Improved FMEA approach for risk evaluation based on hesitant fuzzy set[J]. Computer Integrated Manufacturing Systems,(2016-06-03). http://www.cnki.net/kcms/detail/11.3619.TP.20160603.1613.010.html.
[9] LIU Huchen, LIU Long, LI Ping. Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator[J]. International Journal of Systems Science,2014,45(10):2 012-2 030.
[10] LIU Huchen, YOU Jianxin, SHAN Mengmeng, et al. Failure mode and effects analysis using intuitionistic fuzzy hybrid TOPSIS approach[J]. Soft computing,2015,19(4):1 085-1 098.
[11] ZADEH L A. The concept of a linguistic variable and its application to approximate reasoning-I [J]. Information Science, 1975,8(3):199-249.
[12] CHEN Chentung, CHENG Huiling. A comprehensive model for selecting information system project under fuzzy environment [J]. International Journal of Project Management, 2009,27(4):389-399.
[13] WAN Shuping, LI Dengfeng. Fuzzy LINMAP approach to heterogeneous MADM considering comparisons of alternatives with hesitation degrees [J]. Omega, 2013,41(6):925-940.
[14] BRANS J P,VINCKE P H. A preference ranking organization method the PROMETHEEL method for MCDM[J]. Management Science, 1985,31(6):641-656.
[15] KAHNEMAN Daniel , TVERSKY Amos. Prospect theory: an analysis of decision under risk[J]. Econometrica,1979, 47(2): 263-291.
[16] EBRAHIMNEJAD S, MOUSAVI S M,TAVAKKOLI-MOGHADDAM R, et al. A novel two-phase group decision making approach for construction project selection in fuzzy environment[J]. Applied Mathematical Modeling, 2012,36(9):4 197-4 217.
[17] KUMRU Mesut, KUMRU Pinar Yildiz. Fuzzy FMEA application to improve purchasing process in a public hospital[J]. Applied Soft Computing,2013,13(1):721-733.
[18] SELIM Hasan, YUNUSOGLU Mualla Gonca, BALAMAN Sebnem Yilmaz. A dynamic maintenance planning framework based on fuzzy TOPSIS and FMEA: application in an international food company[J]. Quality and reliability Engineering International, 2016,32(3):795-804.