Skip to Main content Skip to Navigation
Conference papers

Procedure based on mutual information and bayesian networks for fault diagnosis of industrial systems

Abstract : The aim of this paper is to present a new method for process diagnosis using a bayesian network. The mutual information between each variable of the system and the class variable is computed to identify the important variables. To illustrate the performances of this method, we use the Tennessee Eastman Process. For this complex process (51 variables), we take into account three kinds of faults with the minimal recognition error rate objective.
Complete list of metadata

Cited literature [33 references]  Display  Hide  Download

https://hal.inria.fr/inria-00517019
Contributor : Sylvain Verron <>
Submitted on : Monday, September 13, 2010 - 1:49:11 PM
Last modification on : Thursday, May 20, 2021 - 11:46:02 PM
Long-term archiving on: : Tuesday, December 14, 2010 - 2:47:19 AM

File

verron07b.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00517019, version 1

Collections

Citation

Sylvain Verron, Teodor Tiplica, Abdessamad Kobi. Procedure based on mutual information and bayesian networks for fault diagnosis of industrial systems. American Control Conference (ACC'07), 2007, NewYork, United States. ⟨inria-00517019⟩

Share

Metrics

Record views

332

Files downloads

857