论文信息 - Bayesian Network Method for Fault Diagnosis in a Continuous Tubular Reactor

Bayesian Network Method for Fault Diagnosis in a Continuous Tubular Reactor

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.

[1] David Heckerman,et al. Bayesian Networks for Data Mining , 2004, Data Mining and Knowledge Discovery.

[2] 松山秀人,et al. Bubble Production and Foamate Recovery , 2014 .

[3] Frank P. Lees,et al. Loss Prevention In The Process Industries , 1980 .

[4] David Heckerman,et al. A Bayesian Approach to Learning Causal Networks , 1995, UAI.

[5] Douglas Carson,et al. Evaluation of an intensified continuous heat-exchanger reactor for inherently safer characteristics , 2008 .

[6] Francesco Pierri,et al. An integrated approach to fault diagnosis for a class of chemical batch processes , 2009 .

[7] L. Puigjaner,et al. Batch and semibatch reactor performance for an exothermic reaction , 2000 .

[8] Gregory F. Cooper,et al. A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[9] Ronald L. Wasserstein,et al. Monte Carlo: Concepts, Algorithms, and Applications , 1997 .

[10] Lionel Estel,et al. Dynamic model‐based technique for detecting faults in a chemical reactor , 2003 .

[11] A. M. Benkouider,et al. Fault detection in semi-batch reactor using the EKF and statistical method , 2009 .

[12] Masoud Soroush,et al. A method of sensor fault detection and identification , 2005 .