Fuzzy Inference Engine in Condition Monitoring of Industrial Equipment: An Overview

Fuzzy logic systems are intelligent systems applied in equipment condition monitoring. Fuzzy logic systems are well known for their ability to deal with data complexity for better analysis and interpretation. A basic fuzzy logic system consists of four essential parts, among which is the fuzzy inference engine. A fuzzy inference engine is a framework that describes the actual process of converting inputs into outputs using fuzzy logic. However, some challenges have been recognizable in the fuzzy inference engine. Given this, the study aims to systematically review existing literature trends on approaches used for determining parameters of the fuzzy inference system. Specifically, the study examines the methods of assigning membership functions and rules. The study further discusses the contributions of membership functions and rules on fuzzy inference and fuzzy logic systems. Then the study concludes that the system’s parameters are configured based on the problem size and type. Also, there is no standard method of defining or improving parameters to advance the system’s performance. Furthermore, a substantial interrelation between parameters affects the inference system results. Therefore, the study also proposes new ideas for expanding the literature on inference system parameters to improve the overall system’s effectiveness. Moreover, the study recommended future research to be on studies that investigate the contribution of individual parameters on the inference engine. This will enable monitoring of the parameter’s level of impact and the shortcomings of these effects on the inference engine performance. Lastly, a study focusing on the rule reduction methods and the acceptable conditions for carrying out rule reduction is recommended. This study is of importance since indistinct information has been provided on rule reduction, even though it proves to be effective in altering the performance and accuracy of the inference system.