论文标题

主动维护的优化模糊逻辑模型

An optimized fuzzy logic model for proactive maintenance

论文作者

Kerarmi, Abdelouadoud, Kamal-idrissi, Assia, Seghrouchni, Amal El Fallah

论文摘要

在以前的机器诊断研究中提出了模糊逻辑,以克服传统诊断方法的不同缺点。在这些方法中,故障模式和效果批判分析方法(FMECA)试图在基于主观专家判断的情况下识别潜在模式并治疗失败。尽管使用了几种版本的模糊逻辑来改善FMECA或替换它,但由于它是一种非常昂贵的方法,因为它可以单独评估它们,因此这些命题并未明确地专注于组合复杂性,也没有证明在模糊逻辑建模中的成员函数合理。在此上下文中,我们开发了一种基于优化的方法,它引用了集成的真实表和模糊逻辑模型(ITTFLM),该模型(ITTFLM)巧妙地使用真实表生成模糊逻辑规则。 ITTFLM是根据工厂机器实时收集的风扇数据测试的。在实验中,使用了三种类型的成员函数(三角形,梯形和高斯)。 ITTFLM可以以5ms的形式生成输出,结果表明,基于梯形成员资格功能的该模型以高精度来识别故障状态,并且其处理大量规则的能力,因此满足了通常会影响用户体验的实时约束。

Fuzzy logic has been proposed in previous studies for machine diagnosis, to overcome different drawbacks of the traditional diagnostic approaches used. Among these approaches Failure Mode and Effect Critical Analysis method(FMECA) attempts to identify potential modes and treat failures before they occur based on subjective expert judgments. Although several versions of fuzzy logic are used to improve FMECA or to replace it, since it is an extremely cost-intensive approach in terms of failure modes because it evaluates each one of them separately, these propositions have not explicitly focused on the combinatorial complexity nor justified the choice of membership functions in Fuzzy logic modeling. Within this context, we develop an optimization-based approach referred to Integrated Truth Table and Fuzzy Logic Model (ITTFLM) that smartly generates fuzzy logic rules using Truth Tables. The ITTFLM was tested on fan data collected in real-time from a plant machine. In the experiment, three types of membership functions (Triangular, Trapezoidal, and Gaussian) were used. The ITTFLM can generate outputs in 5ms, the results demonstrate that this model based on the Trapezoidal membership functions identifies the failure states with high accuracy, and its capability of dealing with large numbers of rules and thus meets the real-time constraints that usually impact user experience.

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