基于多属性群决策的加工中心故障模式风险分析

doi:10.13229/j.cnki.jdxbgxb20211147

摘要/Abstract

摘要：

针对传统数控机床故障模式及影响分析（FMEA）中存在专家权重与风险因子权重分配不合理以及风险系数（RPN）计算模型不够稳健的问题，提出一种基于多属性群决策的改进FMEA方法。首先，引入区间数表征机床故障模式风险因子；其次，考虑专家的主、客观权重，根据一致性原则，计算专家综合权重，并利用加权平均算子（WAA）确定故障模式综合评价矩阵；再次，采用区间数熵值法确定风险因子权重，利用改进风险优先数（IRPN）计算模型得到故障模式风险值；最后，结合区间数距离测度，采用最优最劣区间数改进的逼近理想解排序方法（TOPSIS）对故障模式进行风险排序。以某型加工中心为例进行方法应用，验证了方法的合理性和有效性。

关键词: 机床, 故障模式及影响分析, 多属性群决策, 改进风险优先数, 逼近理想解排序方法

Abstract:

Aiming at the problems existing in the traditional FMEA of CNC machine tools， such as the unreasonable distribution of expert weight and risk factor weight， and the insufficient robustness of the calculation model of risk coefficient（RPN）， an improved FMEA method based on multi-attribute group decision-making is proposed. Firstly， interval number is introduced to represent the risk factor of machine tool failure mode； Secondly， considering the subjective and objective weights of experts， according to the consistency principle， the comprehensive weights of experts are calculated， and the weighted average operator（WAA） is used to determine the comprehensive evaluation matrix of fault mode； Thirdly， the interval number entropy method is used to determine the weight of risk factors， and the improved risk priority number （IRPN） calculation model is used to obtain the failure mode risk value； Finally， combined with interval number distance measure， the optimal and worst interval number improved TOPSIS method is used to rank the risk of failure mode. Taking a machining center as an example， the rationality and effectiveness of the method are verified.

Key words: machine tool, failure mode and effect analysis, multi-attribute group decision-making, improved risk priority number, technique for order preference by similarity to an ideal solution

中图分类号:

TG659

申桂香,郑君,张英芝,宋杰,李哲文. 基于多属性群决策的加工中心故障模式风险分析[J]. 吉林大学学报(工学版), 2022, 52(2): 338-344.

Gui-xiang SHEN,Jun ZHENG,Ying-zhi ZHANG,Jie SONG,Zhe-wen LI. Risk analysis of machining center failure mode based on multi⁃attribute group decision making[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 338-344.

图/表 7

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故障	OPR	ESR	IRPN	F_i
H₁	［5.4928，6.1592］	［6.5194，7.4086］	［5.9176，6.6743］	0.5966
H₂	［3.4982，4.3731］	［4.3769，5.0072］	［3.8563，4.6384］	0.0000
H₃	［8.2417，8.4395］	［7.1791，7.9644］	［7.7616，8.2294］	1.0000
H₄	［3.7259，4.1042］	［6.4459，6.7387］	［4.7286，5.0917］	0.0507
H₅	［3.8337，4.2242］	［6.5900，7.2762］	［4.8519，5.3507］	0.0849
H₆ H₇ H₈ H₉ H₁₀ H₁₁ H₁₂ H₁₃ H₁₄	［3.7532，3.9995］［4.1898，4.3029］［3.8079，3.8709］［5.4130，6.1102］［4.6526，5.0433］［3.6416，4.6526］［3.5768，3.8921］［4.5566，5.1622］［4.5014，5.0735］	［6.3354，6.9485］［4.8352，5.6408］［6.8116，7.1112］［5.8572，6.1702］［5.1228，5.8277］［4.5910，5.1108］［6.1377，6.7498］［6.5826，7.1726］［7.0718，7.5394］	［4.7127，5.0850］［4.4591，4.8404］［4.9033，5.0426］［5.6018，6.1363］［4.8515，5.3705］［4.0276，4.8465］［4.5233，4.9447］［5.3469，5.9558］［5.4784，6.0269］	0.0488 0.0200 0.0645 0.3791 0.0868 0.0024 0.0271 0.2677 0.3148