Robust Fault Diagnosis in Catalytic Cracking Converter Using Artificial Neural Networks
نویسنده
چکیده
The paper presents designing of a robust fault diagnosis system for a catalytic cracking process using artificial neural networks. Identification of the considered process is carried out by using recurrent neural networks. To achieve a robust fault diagnosis system, an uncertainty associated with the model is also taken into account. Neural version of the Model Error Modelling is used to deal with two main uncertainty sources: unmodelled dynamics and noise corrupting the data. The proposed approach is tested on the example of catalytic cracking converter at the nominal operations condition as well as in the case of faults. Copyright c © 2005 IFAC
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