TY - GEN
T1 - Fuzzy Inference Engine in Condition Monitoring of Industrial Equipment
T2 - 14th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2023
AU - Kgatwe, Calvinia K.
AU - Olatunji, Obafemi O.
AU - Adedeji, Paul A.
AU - Madushele, Nkosinathi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - and industrial equipment
KW - condition monitoring
KW - fuzzy inference engine
KW - fuzzy logic systems
UR - http://www.scopus.com/inward/record.url?scp=85168768437&partnerID=8YFLogxK
U2 - 10.1109/ICMIMT59138.2023.10200882
DO - 10.1109/ICMIMT59138.2023.10200882
M3 - Conference contribution
AN - SCOPUS:85168768437
T3 - 2023 14th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2023
SP - 262
EP - 267
BT - 2023 14th International Conference on Mechanical and Intelligent Manufacturing Technologies, ICMIMT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 May 2023 through 28 May 2023
ER -