Othman, Mahamad Rizza and Ali, Mohamad Wijayanuddin and Kamsah, Mohd. Zaki (2007) Process fault detection using hierarchical artificial neural network diagnostic strategy. Jurnal Teknologi (46F). pp. 11-26. ISSN 0127-9696
Official URL: http://www.penerbit.utm.my/onlinejournal/46/F/JTJu...
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.
|Uncontrolled Keywords:||process fault detection and diagnosis, hierarchical diagnostic strategy, artificial neural network, fatty acid fractionation column|
|Subjects:||T Technology > TP Chemical technology|
|Divisions:||Chemical and Natural Resources Engineering (Formerly known)|
|Deposited By:||Norshiela Buyamin|
|Deposited On:||27 Mar 2009 07:22|
|Last Modified:||28 Oct 2010 04:37|
Repository Staff Only: item control page