Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
381720 | Engineering Applications of Artificial Intelligence | 2007 | 12 Pages |
Abstract
The paper deals with a model-based fault diagnosis for a catalytic cracking converter process realized using artificial neural networks. Modelling of the considered process is carried out by using a locally recurrent neural network. Decision making about possible faults is performed using statistical analysis of a residual. A neural network is applied to density shaping of a residual. After that, assuming a significance level, a threshold is calculated. The proposed approach is tested on the example of a catalytic cracking converter at the nominal operating conditions as well as in the case of faults.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Krzysztof Patan, Józef Korbicz,