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DOI: 10.5121/csit.2022.122303

An optimized fuzzy logic model for proactive maintenance

Abdelouadoud KerarmiAssia Kamal-idrissiAmal El Fallah Seghrouchni
Dec 2022
摘要
Fuzzy logic has been proposed in previous studies for machine diagnosis, toovercome 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 basedon subjective expert judgments. Although several versions of fuzzy logic areused to improve FMECA or to replace it, since it is an extremely cost-intensiveapproach in terms of failure modes because it evaluates each one of themseparately, these propositions have not explicitly focused on the combinatorialcomplexity nor justified the choice of membership functions in Fuzzy logicmodeling. Within this context, we develop an optimization-based approachreferred to Integrated Truth Table and Fuzzy Logic Model (ITTFLM) that smartlygenerates fuzzy logic rules using Truth Tables. The ITTFLM was tested on fandata collected in real-time from a plant machine. In the experiment, threetypes of membership functions (Triangular, Trapezoidal, and Gaussian) wereused. The ITTFLM can generate outputs in 5ms, the results demonstrate that thismodel based on the Trapezoidal membership functions identifies the failurestates with high accuracy, and its capability of dealing with large numbers ofrules and thus meets the real-time constraints that usually impact userexperience.
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