Hybrid manufacturing line supervision and diagnosis by means of fuzzy rules connected with a causal graph

Author

Chevalier, Emmanuel ; Martin, Joseph Aguilar ; Colomb, Gil Blanch i ; Laserna, J.L.M.

Author_Institution

LAAS, CNRS, Toulouse, France

Volume

3

fYear

1997

fDate

1-5 Jul 1997

Firstpage

1259

Abstract

The method proposed here consists in generating reactive knowledge represented by fuzzy rules from a given causal graph model. Causal reasoning forms a practical support for model-based diagnosis. Also the fuzzy logic allows us to generate the corrective actions for a qualitative model-based system (supervision). These two kinds of reasoning have been integrated in a computer system for a real world application. A global model based on a causal graph of the process is used for the diagnostic, but local fuzzy reasoning blocks are used for supervision

Keywords

common-sense reasoning; fault diagnosis; fuzzy logic; graph theory; knowledge acquisition; manufacturing processes; model-based reasoning; process control; causal graph; causal reasoning; corrective actions; diagnosis; fuzzy logic; fuzzy rules; global model; local fuzzy reasoning blocks; manufacturing line; model-based diagnosis; qualitative model-based system; reactive knowledge; supervision; Application software; Computer aided manufacturing; Computer industry; Fuzzy logic; Fuzzy reasoning; Fuzzy systems; Humans; Hybrid power systems; Manufacturing processes; Virtual manufacturing;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1997., Proceedings of the Sixth IEEE International Conference on

Conference_Location

Barcelona

Print_ISBN

0-7803-3796-4

Type

conf

DOI

10.1109/FUZZY.1997.619468

Filename

619468