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...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Othman, Mahamad Rizza, Ali, Mohamad Wijayanuddin, Kamsah, Mohd. Zaki
التنسيق: مقال
اللغة:English
English
منشور في: Penerbit UTM Press 2007
الموضوعات:
الوصول للمادة أونلاين: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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المؤسسة: Universiti Teknologi Malaysia
اللغة: English
English
الوصف
الملخص: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.