An Approach to Knowledge Discovery for Fault Detection by Using Compensatory Fuzzy Logic

Failure diagnosis and prevention are crucial areas of interest for the proposal of innovative methods and techniques that can help to increase the availability of industrial machinery and other complex systems. In this work we propose a Knowledge Discovery scheme, based on a Compensatory Fuzzy Logic (CFL), for failure detection and prevention. With an exploratory approach, the proposed methodology includes obtaining a characterization of operating conditions of a system, which can be useful for detecting harmful conditions. As a case of study we obtain data of operating conditions of a direct current (DC) motor. A set of fuzzy predicates are formulated and evaluated using the degrees of membership of the variables of the motor to adequate fuzzy membership functions. The truth values resulting of such evaluations are analyzed in view of the empiric knowledge of failures occurrence of DC motors. The main contribution of this work is to explore the possible advantages of using the compensatory fuzzy logic approach for fuzzy predicate evaluation for fault detection and prevention, which could be applied later to more complex systems.