A self-validating inferential sensor for emission monitoring

Author

Qin, S.Joe ; Yue, Hongyu ; Dunia, Ricardo

Author_Institution

Dept. of Chem. Eng., Texas Univ., Austin, TX, USA

Volume

1

fYear

1997

fDate

4-6 Jun 1997

Firstpage

473

Abstract

Proposes a self-validating inferential sensor approach based on principal component analysis (PCA). The input sensors are validated using a fault identification and reconstruction approach proposed in Dunia et al. (1996). A principal component model is built for the input sensor validation. The validated principal components are used to predict output variables using linear regression or neural networks. If a sensor fails, the sensor is identified and reconstructed with the best estimate from the PCA model. The principal components are also reconstructed accordingly for prediction. The self-validating soft sensor approach is applied to air emission monitoring

Keywords

air pollution measurement; environmental science computing; fault location; inference mechanisms; neural nets; sensor fusion; statistical analysis; PCA; air emission monitoring; fault identification; fault reconstruction; input sensor validation; linear regression; neural networks; principal component analysis; self-validating inferential sensor; self-validating soft sensor; Chemical engineering; Chemical sensors; Electrical equipment industry; Input variables; Matrix decomposition; Monitoring; Neural networks; Predictive models; Principal component analysis; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1997. Proceedings of the 1997

Conference_Location

Albuquerque, NM

ISSN

0743-1619

Print_ISBN

0-7803-3832-4

Type

conf

DOI

10.1109/ACC.1997.611844

Filename

611844