UMP Institutional Repository

Process Fault Detection Using Hierarchical Artificial Neural Network Diagnostic Strategy

Mohamad Rizza, Othman and Mohamad Wijayanuddin, Ali and Mohd Zaki, Kamsah (2007) Process Fault Detection Using Hierarchical Artificial Neural Network Diagnostic Strategy. Jurnal Teknologi (Sciences and Engineering), 46. pp. 11-26. ISSN 0127-9696 (print); 2180-3722 (online)

[img] PDF
Restricted to Repository staff only

Download (184kB) | Request a copy
fkksa-2007-rizza-Process Fault Detection.pdf

Download (38kB) | Preview


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.

Item Type: Article
Uncontrolled Keywords: Process fault detection and diagnosis, hierarchical diagnostic strategy, artificial neural network, fatty acid fractionation column
Subjects: T Technology > TP Chemical technology
Faculty/Division: Faculty of Chemical & Natural Resources Engineering
Depositing User: Ms. Hazima Anuar
Date Deposited: 05 May 2016 00:53
Last Modified: 09 Mar 2018 07:20
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item