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.
翻译:在以往的机器诊断研究中,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,失败模式和效果关键分析方法(FMECA)试图根据主观专家判断确定潜在模式和在失败发生之前先处理失败。虽然使用了若干版本的模糊逻辑来改进或取代FMECA, 因为它在失败模式方面是一种成本极高的方法,因为它分别评估了其中的每一个模式,因此在失败模式方面是一种成本极高的方法,这些提议没有明确侧重于组合复杂性,也没有证明选择Fuzzy逻辑模型中的成员资格功能是合理的。在此背景下,我们开发了一种基于优化的方法,用于综合真相表和模糊逻辑模型(ITTFLM),在它们出现失败之前,使用“真相表”巧妙地生成模糊的逻辑规则。ITTFLM用从工厂机器实时收集的迷你数据进行了测试。在实验中,使用了三种会员功能(Trigraphal、Trapioidal和Gaussian)。ITFLM可以产生5M的输出结果。在这种模型中,结果表明,这种模型基于经常交易规则的高度的准确性以及用户的失败,从而确定其实际的失败。