Skip to Main content Skip to Navigation
Journal articles

Bayesian Network Method for Fault Diagnosis in a Continuous Tubular Reactor

Abstract : The aim is this paper is to study fault diagnosis in a continuous chemical process. An experimental system is built to be the research base and a model is proposed and trained to carry out the fault diagnosis in the process. A Bayesian network model with two-layer nodes structure is designed and Maximum likelihood estimation (MLE) is used to amend the conditional probability table (CPT) given by expert knowledge. Then a Monte Carlo method is applied to simplify the inference rules and the data samples collected from the experimental system has been used to test the model.
Document type :
Journal articles
Complete list of metadatas

https://hal-normandie-univ.archives-ouvertes.fr/hal-02409976
Contributor : Isabelle Polaert <>
Submitted on : Friday, December 13, 2019 - 4:06:54 PM
Last modification on : Thursday, February 6, 2020 - 9:18:02 AM

Links full text

Identifiers

Citation

Haoran Liu, Jean-Christophe Buvat, Lionel Estel, Isabelle Polaert. Bayesian Network Method for Fault Diagnosis in a Continuous Tubular Reactor. Chemical Product and Process Modeling, De Gruyter, 2010, 5 (1), ⟨10.2202/1934-2659.1491⟩. ⟨hal-02409976⟩

Share

Metrics

Record views

37