Diagnosing from descriptive knowledge bases

Author

Filev, Dimitax P.

Author_Institution

Ford Motor Co., Detroit, MI, USA

fYear

1997

fDate

21-24 Sep 1997

Firstpage

440

Lastpage

443

Abstract

Deals with a diagnostic method using so-called descriptive knowledge bases. It is based on the concept of using a MIMO (multiple-input, multiple output) fuzzy model to represent the information contained in the knowledge base. The model structure and initial parameter values are estimated by applying FMEA (failure mode and effect analysis). Finer parameter estimates are obtained by using a backpropagation learning algorithm. Rules for learning the parameters of a MIMO fuzzy model are derived. An actual diagnostic problem is solved by using an an optimization algorithm for inverting the MIMO fuzzy model

Keywords

MIMO systems; backpropagation; diagnostic expert systems; diagnostic reasoning; failure analysis; fault diagnosis; fuzzy logic; knowledge representation; optimisation; parameter estimation; MIMO fuzzy model; backpropagation learning algorithm; descriptive knowledge bases; diagnostic method; failure mode and effect analysis; information representation; initial parameter value estimation; model inversion; model structure estimation; optimization algorithm; Backpropagation algorithms; Diagnostic expert systems; Failure analysis; Kernel; Knowledge engineering; MIMO; Manufacturing industries; Parameter estimation; Personnel; Virtual manufacturing;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 1997. NAFIPS '97., 1997 Annual Meeting of the North American

Conference_Location

Syracuse, NY

Print_ISBN

0-7803-4078-7

Type

conf

DOI

10.1109/NAFIPS.1997.624081

Filename

624081