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.

道路事故已成为全世界的八项主要死亡原因。这些事故中有很多是由于驾驶员的注意力不集中或由于疲劳而缺乏专注。各种因素导致驾驶员的疲劳。本文考虑了表现出驾驶员疲劳的所有可测量数据，即在车辆可测量数据中表现出的疲劳以及驾驶员的物理和生理数据。这三个主要因素中的每个因素都进一步细分为较小的细节。例如，车辆的数据由从方向盘的角度，偏航角，车道上的位置以及移动时车辆的速度和加速度获得的值组成。驾驶员疲劳检测的本体论知识和规则将集成到智能系统中，以便在检测到危险疲劳水平的第一个迹象时，将警告通知发送给驾驶员。这项工作旨在为安全的道路驾驶做出贡献。