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Journal Articles Chemical Product and Process Modeling Year : 2010

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.

Dates and versions

hal-02409976 , version 1 (13-12-2019)

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Cite

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, 2010, 5 (1), pp.Article 28. ⟨10.2202/1934-2659.1491⟩. ⟨hal-02409976⟩
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