A new statistical pattern recognition distance rejection model: application to the monitoring of car catalytic converters

Author

Boatas, A. ; Dubuisson, B. ; Dillies-Peltier, M.A.

Author_Institution

PSA Peugeot Citroen, Velizy Villacoublay, France

Volume

4

fYear

2000

fDate

2000

Firstpage

2839

Abstract

A novel statistical pattern recognition model is proposed in order to solve specific industrial diagnosis problems. The representation of discriminative parameters as a function of the operating point parameters enables accurate operating mode distance rejection. Adapted distance rejection options are presented in order to deal with unknown classes. These methods have been applied to a real world diagnosis problem: the monitoring of car catalytic converters

Keywords

automobiles; fault diagnosis; learning systems; pattern recognition; statistical analysis; accurate operating mode distance rejection; adapted distance rejection options; car catalytic converter monitoring; discriminative parameters; industrial diagnosis problems; operating point parameters; real world diagnosis problem; statistical pattern recognition distance rejection model; unknown classes; Chromium; Condition monitoring; Costs; Electronic mail; Error probability; Exhaust systems; Learning systems; Pattern recognition; Probability density function;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 2000 IEEE International Conference on

Conference_Location

Nashville, TN

ISSN

1062-922X

Print_ISBN

0-7803-6583-6

Type

conf

DOI

10.1109/ICSMC.2000.884428

Filename

884428