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Process fault detection and diagnosis using a dynamic neural networks model


Citation

Abdul Rahman, Ribhan Zafira and Che Soh, Azura and Ahmad, Erny Arniza and Mohd Noor, Samsul Bahari and Gomm, J. B. (2002) Process fault detection and diagnosis using a dynamic neural networks model. In: 2nd World Engineering Congress, 22-25 July 2002, Sarawak, Malaysia. (pp. 401-406).

Abstract / Synopsis

Recently, neural networks has generated considerable interest as an alternative non-linear modelling tool. The major attraction is the learning capabilities of neural networks, and the fact that multi-layer, feed forward networks can approximate any non-linear function with arbitrary accuracy. This study describes the application of the multi-layer perceptron (MLP) neural network, trained using back-error propagation, to obtain a representative model of a non-linear process over a wide operational region. The purpose of this study is mainly to investigate the use of dynamic neural networks model for fault detection and diagnosis of the process control. The MATLAB with SIMULINK process and Multi-Layer Perceptron Software Package is used as a method to procure the required result.


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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Engineering
Publisher: Universiti Putra Malaysia Press
Keywords: Fault detection; Dynamic neural networks model
Depositing User: Erni Suraya Abdul Aziz
Date Deposited: 28 Apr 2015 16:14
Last Modified: 30 Mar 2018 11:04
URI: http://psasir.upm.edu.my/id/eprint/33839
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