A fuzzy decision system based on statistical learning for fault classifications

Author

Chen, Yubao

Author_Institution

Dept. of Ind. & Manuf. Syst. Eng., Michigan Univ., Dearborn, MI, USA

fYear

1994

fDate

26-29 Jun 1994

Firstpage

1459

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/FUZZY.1994.343909

Filename

343909

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2276255