Process Fault Detection Using Hierarchical Artificial Neural Network Diagnostic Strategy

• Main Authors: Mohamad Rizza, Othman, Mohamad Wijayanuddin, Ali, Mohd Zaki, Kamsah
• Format: Article
• Language: English; English
• Published: Penerbit Universiti Teknologi Malaysia 2007
• Subjects: TP Chemical technology
• Online Access: http://umpir.ump.edu.my/id/eprint/6783/1/Process_Fault_Detection.pdf; http://umpir.ump.edu.my/id/eprint/6783/4/fkksa-2007-rizza-Process%20Fault%20Detection.pdf; http://umpir.ump.edu.my/id/eprint/6783/; http://www.jurnalteknologi.utm.my/index.php/jurnalteknologi/article/view/301/291
• Institution: Universiti Malaysia Pahang

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.