ERTIAME, AM, Yu, DL, Yu, F and Gomm, JB (2014) Robust fault diagnosis for an exothermic semi-batch polymerization reactor under open-loop. Systems Science & Control Engineering, 3 (1). pp. 14-23. ISSN 2164-2583

Preview

Text ICAC2013.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial. Download (1MB) | Preview

Abstract

An independent radial basis function neural network (RBFNN) is developed and employed here for an online diagnosis of actuator and sensor faults. In this research, a robust fault detection and isolation scheme is developed for an open-loop exothermic semi-batch polymerization reactor described by Chylla–Haase. The independent RBFNN is employed here for online diagnosis of faults when the system is subjected to system uncertainties and disturbances. Two different techniques to employ RBFNNs are investigated. Firstly, an independent neural network (NN) is used to model the reactor dynamics and generate residuals. Secondly, an additional RBFNN is developed as a classifier to isolate faults from the generated residuals. Three sensor faults and one actuator fault are simulated on the reactor. Moreover, many practical disturbances and system uncertainties, such as monomer feed rate, fouling factor, impurity factor, ambient temperature and measurement noise, are modelled. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.

Item Type:	Article
Subjects:	T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions:	Electronics & Electrical Engineering (merged with Engineering 10 Aug 20) Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Publisher:	Taylor & Francis Open
Date Deposited:	06 Nov 2017 12:09
Last Modified:	04 Sep 2021 03:39
DOI or ID number:	10.1080/21642583.2014.984356
URI:	https://researchonline.ljmu.ac.uk/id/eprint/7478

View Item