基于改进乘性一致HFLPRs-TOPSIS的FMEA风险分析

PDF(2037 KB)

振动与冲击 ›› 2025, Vol. 44 ›› Issue (11) : 178-187.

振动理论与交叉研究

基于改进乘性一致HFLPRs-TOPSIS的FMEA风险分析

刘子辉1，2，方艳红*1，2，锁斌1，2

作者信息 +

FMEA risk analysis based on improved multiplicative consistency HFLPRs-TOPSIS

LIU Zihui1,2, FANG Yanhong*1,2, SUO Bin1,2

Author information +

文章历史 +

摘要

在可靠性评估的故障模式与影响分析(FMEA)中，专家评分的不确定性和群决策的冲突性显著影响了风险分析结果的准确性。为此，本文提出了一种基于改进的乘性一致犹豫模糊语言偏好关系集(HFLPRs)与逼近理想解(TOPSIS)的风险分析方法。该方法首先引入HFLPRs，系统捕捉指标偏好度；其次，通过改进乘性一致公式并构建HFLPRs的去模糊最优化模型，结合IOWA算子修正专家权重，完成群决策信息的有效提取与聚合；最后，采用改进的TOPSIS对各故障模式进行风险排序。以列车裙板的风险分析作为实例，完成所提方法的敏感性分析与对比性分析。结果表明，所提方式能够更有效的保存专家原始信息，降低来自群体不确定性与冲突性对结果的影响，获得更为准确、客观、稳定的风险分析结果。

Abstract

In the reliability assessment phase of Failure Mode and Effects Analysis (FMEA), the uncertainty in expert ratings and the conflicts in group decision-making significantly impact the accuracy of risk analysis results. To address this issue, this paper proposes a risk analysis method based on an improved multiplicative consistency hesitant fuzzy linguistic preference relation set (HFLPRs) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The method first introduces HFLPRs to systematically capture the preference degrees of the indicators. Next, it improves the multiplicative consistency formula and constructs a defuzzification optimization model for HFLPRs, combining the IOWA operator to adjust expert weights, thereby effectively extracting and aggregating group decision information. Finally, the improved TOPSIS method is employed to rank the risks of various failure modes. Using the risk analysis of train skirts as a case study, sensitivity and comparative analyses of the proposed method are conducted. The results indicate that the proposed approach can more effectively preserve the original information from experts, reduce the impact of uncertainty and conflict within the group on the results, and achieve more accurate, objective, and stable risk analysis outcomes.

导出引用

刘子辉1, 2, 方艳红1, 2, 锁斌1, 2. 基于改进乘性一致HFLPRs-TOPSIS的FMEA风险分析[J]. 振动与冲击, 2025, 44(11): 178-187

LIU Zihui1, 2, FANG Yanhong1, 2, SUO Bin1, 2. FMEA risk analysis based on improved multiplicative consistency HFLPRs-TOPSIS[J]. Journal of Vibration and Shock, 2025, 44(11): 178-187

参考文献

[1] 谭正超,锁斌,杨战平.基于主客观综合赋权的风险优先数分析[J].太赫兹科学与电子信息学报, 2021, 19(06): 1057-1064.
Z. C. Tan, B. Suo, Z. P. Yang. Risk priority number analysis based on subjective and objective integrated weighting[J].Journal of Terahertz Science and Electronic Information Technology, 2021, 19(06): 1057-1064.
[2] K. Cicek , M. Celik . Application of failure modes and effects analysis to main engine crankcase explosion failure on-board ship[J]. Safety Science, 2013, 51(1): 6-10.
[3] 韦可佳,耿俊豹,徐孙庆.基于模糊理论与D-S证据理论的FMEA方法[J].系统工程与电子技术, 2019, 41(11): 2662-2668.
K. J. Wei, J. B. Gen, S. Q. Xu. FMEA method based on fuzzy theory and D-S evidence theory[J].Systems Engineering and Electronics, 2019, 41(11): 2662-2668.
[4] P. Bhattacharjee , V. Dey , U. K. Mandal . Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model[J]. Safety Science, 2020, 132(8):104967.
[5] 门峰.模糊集理论与灰色关联理论的FMEA方法[J].工业工程, 2008, 4: 109-112.
F. Men. FMEA Method Based upon Fuzzy Set Theory and Grey Relational Theory[J].Industrial Engineering Journal, 2008, 4: 109-112.
[6] 张孟飞,王铁旦,彭定洪,任子瑞.基于犹豫模糊偏好关系FMEA方法的改进[J].运筹与管理, 2021, 30(5): 73-78.
M.F. Zhang, T. D. Wang, D. H. Peng, Z. R. Ren. Improvement of FMEA Method Based on Hesitative Fuzzy Preference Relation[J]. Operations Research and Management Science, 2021, 30(5): 73-78.
[7] 鞠萍华,陈资,冉琰,胡晓波.多粒度概率语言环境下基于PROMETHEE的改进FMEA方法[J].北京航空航天大学报, 2019, 45(11): 2266-2276.
P. H. Ju , Z. Chen , T. Lan, X. B. Hu. Improved FMEA method based on PROMETHEE in multi-granular probabilistic linguistic environment[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2266-2276.
[8] X. Jian, H. C. Yang, L. Zhang. A weakest t-norm based fuzzy fault tree approach for leakage risk assessment of submarine pipeline[J]. Loss Prev Process Ind, 2019, 62: 103968.
[9] S Zolfaghari, S. M. Mousavi. A new risk evaluation methodology based on FMEA, MULTIMOORA, TPOP, and interval-valued hesitant fuzzy linguistic sets with an application to healthcare industry[J]. Kybernetes, 2021, 50: 2521-2547.
[10] Z. C. Wang, R Yan, Y. F. Chen. Group risk assessment in failure mode and effects analysis using a hybrid probabilistic hesitant fuzzy linguistic MCDM method[J]. Expert Systems with Applications, 2022, 188: 116013.
[11] 欧阳中辉,胡道畅,陈青华,樊辉锦.基于模糊集理论和TOPSIS的FMEA分析方法[J].兵部装备工程学报,2020，41(11):117-123.
Z. H. OuYang, D. C. Hu, Q. H. Chen, H. J. Fan. Research on FMEA Analysis Method of Diesel Engine Based on Fuzzy Set Theory and TOPSIS[J]. Journal of Ordnance Equipment Engineering, 2020, 41(11): 117-123.
[12] 石旭东,成博源,黄琨,杨占刚.基于模糊TOPSIS-FMEA的飞机IDG风险评价[J].系统工程与电子技术, 2022, 44(06): 2060-2064.
X. D. Shi ,B. Y. Chen ,K. Huang , Z. G. Yang. Risk assessment of aircraft IDG based on fuzzy TOPSIS-FMEA[J]. Systems Engineering and Electronics, 2022, 44(06): 2060-2064.
[13] M Mehri,S Alireza,H S Faranak.Improvement of risk assessment in the FMEA using nonlinear model, revised fuzzy TOPSIS and support vector machine[J].International Journal of Industrial Ergonomics, 2019, 69: 209-216.
[14] Z. L. Yue.An extended TOPSIS for determining weights of decision makers with interval numbers[J].Knowledge-Based Systems, 2010(24): 146-153.
[15] M. M. Xia, Z. S. Xu. Hesitant fuzzy information aggregation in decision making [J].International Journal of Approximate Reasoning, 2011, 52(3): 395-407.
[16] Rodriguez, R. M. Martinez, L. Herrera. Hesitant fuzzy linguistic term sets for decision making [J]. IEEE Transactions on Fuzzy Systems, 2012(20): 109-119.
[17] Z. S. Xu. A method based on linguistic aggregation operators for group decision making with linguistic preference relations [J]. Information Sciences, 2004, 166(4): 19-30.
[18] T. Tanino. Fuzzy preference orderings in group decision making [J]. Fuzzy Sets and Systems, 1984, 12(2): 117-131.
[19] F. Chiclana, E. Herrera-Viedma, F. Herrera, S. Alonso. Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations [J]. European Journal of Operational Research, 2007, 182: 383-399.
[20] Z. Wang , Y. Ran, Y. Chen , et al. Failure mode and effects analysis using extended matter-element model and AHP[J]. Computers & Industrial Engineering, 2020, 140:106233.1-106233.8.
[21] 高萍.基于可靠性分析的复杂设备预防性维修决策研究[D].清华大学,2009(08).
P. Gao.Research on preventive maintenance decision of complex equipment based on reliability analysis[D], Tsinghua University, 2009(08).
[22] Wang L E , Liu H C , Quan M Y . Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments[J]. Computers & Industrial Engineering, 2016, 102:175-185.
[23] Z. L. Yue.An extended TOPSIS for determining weights of decision marks with interval numbers[J]. Knowledge-Based Systems, 2011, 24(1): 146-153.
[24] H. W. Wu, E. Q. Li, Y. Y. Sun. Research on the operation safety evaluation of urban rail stations based on the improved TOPSIS method and entropy weight method[J].Journal of Rail Transport Planning & Management, 2021, 20: 100262.
[25] S. S. Wang, W. D. Yuan, Group decision-making method with trust-based weight and reliability parameters[J]Information Sciences. 2024, 662: 120089.
[26] Y. Du, J. Zhong, Generalized combination rule for evidential reasoning approach and Dempster-Shafer theory of evidence[J]. Inf. Sci. 2021, 547: 1201–1232.
[27] L. T. Jing, X. Y. Fang, D. Feng,et al. A patent text-based product conceptual design decision-making approach considering the fusion of incomplete evaluation semantic and scheme beliefs[J] Applied Soft Computing, 2024, 157 :111492.