Skip to Main content Skip to Navigation
Conference papers

New Informative Features for Fault Diagnosis of Industrial Systems by Supervised Classification

Abstract : The purpose of this article is to present a method for industrial process diagnosis. We are interested in fault diagnosis considered as a supervised classication task. The interest of the proposed method is to take into account new features (and so new informations) in the classifier. These new features are probabilities extracted from a Bayesian network comparing the faulty observations to the normal operating conditions. The performances of this method are evaluated on the data of a benchmark example: the Tennessee Eastman Process. Three kinds of fault are taken into account on this complex process. We show on this example that the addition of these new features allows to decrease the misclassification rate.
Complete list of metadata

Cited literature [25 references]  Display  Hide  Download

https://hal.inria.fr/inria-00517027
Contributor : Sylvain Verron Connect in order to contact the contributor
Submitted on : Monday, September 13, 2010 - 1:59:42 PM
Last modification on : Wednesday, October 20, 2021 - 3:19:19 AM
Long-term archiving on: : Tuesday, December 14, 2010 - 2:48:22 AM

File

verron09a.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00517027, version 1

Collections

Citation

Sylvain Verron, Teodor Tiplica, Abdessamad Kobi. New Informative Features for Fault Diagnosis of Industrial Systems by Supervised Classification. Workshop on Advanced Control and Diagnosis (ACD'09), 2009, Zielona Gora, Poland. ⟨inria-00517027⟩

Share

Metrics

Record views

55

Files downloads

138