  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.
