Title :
A fuzzy decision system based on statistical learning for fault classifications
Author_Institution :
Dept. of Ind. & Manuf. Syst. Eng., Michigan Univ., Dearborn, MI, USA
Abstract :
A fuzzy decision system (FDS) is proposed for condition monitoring of machining processes. The membership functions are established through a learning process based on test data, rather than being selected as a priori. The optimal partition and information gain weighting functions are also introduced in order to improve the robustness and reliability of this method. Experiment verification with an optimistic success rate of 97.5% was achieved
Keywords :
decision theory; fault diagnosis; fuzzy set theory; fuzzy systems; learning (artificial intelligence); machine tools; machining; condition monitoring; fault classifications; fuzzy decision system; information gain weighting functions; machining processes; membership functions; reliability; statistical learning; test data; Condition monitoring; Fuzzy sets; Fuzzy systems; Machining; Manufacturing industries; Manufacturing systems; Robustness; Statistical learning; Systems engineering and theory; Testing;
Conference_Titel :
Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1896-X
DOI :
10.1109/FUZZY.1994.343909
