Application of Fuzzy Matter-element Method in Reliability Analysis of Meta-action Unit
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摘要: 动作是机电产品的基本运动单元,为了保障整机的可靠性,首先必须保障基本动作单元的可靠性,本文基于模糊物元法提出一种对产品最小动作单元进行可靠性分析的方法-基于模糊物元的元动作单元可靠性分析方法。首先按照“功能-运动-动作”对整机功能进行结构化分解得到最基本的元动作单元;通过模糊物元分析方法提取出相似的元动作单元,并构建出用于元动作单元可靠性分析的失效数据;然后选用三参数威布尔分布模型对失效数据建模和可靠性分析,并利用K-S检验方法对元动作单元的失效数据分布进行检验,以判断选用的分析模型是否合理。以某加工中心数控转台的回转油缸活塞移动元动作单元为例进行分析,验证了本文方法的合理性与可行性。结果表明该元动作单元的失效数据服从三参数威布尔分布模型。Abstract: Action is the basic motion unit of electromechanical products, to ensure the reliability of the whole machine system, the reliability of the basic action unit must be guaranteed. Based on fuzzy matter-element method, a new method for reliability analysis of electromechanical product minimum action unit was proposed. Firstly, according to the structural decomposition mentality of "function-motion-action", the basic meta-action unit can be obtained by structuring decomposition the whole machine function. Then, the fuzzy matter-element analysis method was used to obtain the similar meta-action unit; the failure data of meta-action was constructed based on the similar meta-action, and a three-parameter Weibull distribution model was used to model the failure data. Finally, the failure data distribution of the meta-action unit was tested by Kolmogorov-Smirnov test method to judge whether the selected model was reasonable or not. The rotary cylinder piston moving meta-action of numerical control rotary table of some machining center was taken as an example to test reasonableness and effectiveness of the method. The results show that the failure data of meta-action obey the three-parameter Weibull distribution model, and the research of this paper is also the basis of the mapping between the reliability of the meta-action unit and the reliability of the whole machine.
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表 1 各个特征的权重
指标 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 wj 0.0625 0.0714 0.0285 0.0619 0.0217 0.0654 0.0726 0.0752 0.0798 0.0381 0.1042 0.1103 0.1181 0.0497 0.0405 表 2 失效数据
序号i 1 2 3 4 5 6 7 8 失效时间/h 113.6 129.8 163.4 218.2 249.6 343.7 366.5 422.9 表 3 参数估计值
γ β η 113.6 1.9236 163.74 -
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