Paper Info
Title
Fault detection in catalytic cracking converter by means of probability density approximation
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.
Year
DOI
Venue
2007
10.1016/j.engappai.2006.12.009
Eng. Appl. of AI
Keywords
DocType
Volume
statistical analysis,operant conditioning,artificial neural network,fault detection,identification,probability density,catalytic cracking,neural network
Journal
20
Issue
ISSN
Citations 
7
Engineering Applications of Artificial Intelligence
2
PageRank 
References 
Authors
0.39
6
2
Name
Order
Citations
PageRank
Krzysztof Patan115118.13
Józef Korbicz212716.23