Process fault detection using hierarchical artificial neural network diagnostic strategy

This paper focuses on the use of artificial neural network (ANN) to detect and diagnose fault in process plant. In this work, the ANN uses two layers of hierarchical diagnostic strategy. The first layer diagnoses the node where the fault originated and the second layer classifies the type of faults...

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Main Authors: Othman, Mahamad Rizza, Ali, Mohamad Wijayanuddin, Kamsah, Mohd. Zaki
格式: Article
語言:English
English
出版: Penerbit UTM Press 2007
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在線閱讀:http://eprints.utm.my/id/eprint/8104/3/MohamadWijayanuddinAli2007_ProcessFaultDetectionUsingHierarchicalArtificial.pdf
http://eprints.utm.my/id/eprint/8104/4/291
http://eprints.utm.my/id/eprint/8104/
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機構: Universiti Teknologi Malaysia
語言: English
English
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總結:This paper focuses on the use of artificial neural network (ANN) to detect and diagnose fault in process plant. In this work, the ANN uses two layers of hierarchical diagnostic strategy. The first layer diagnoses the node where the fault originated and the second layer classifies the type of faults or malfunctions occurred on that particular node. The architecture of the ANN model is founded on a multilayer feed forward network and used back propagation algorithm as the training scheme. In order to find the most suitable configuration of ANN, a topology analysis is conducted. The effectiveness of the method is demonstrated by using a fatty acid fractionation column. Results show that the system is successful in detecting original single and transient fault introduced within the process plant model.