The University of Southampton
University of Southampton Institutional Repository

Fault Detection for a Class of Unknown Nonlinear Systems via Associative Memory Networks

Wang, H., Brown, M. and Harris, C.J. (1994) Fault Detection for a Class of Unknown Nonlinear Systems via Associative Memory Networks Proc. I Mech E, J. Systems and Control Engr., 208, (12), 101--108.

Record type: Article


This paper presents a novel approach for the detection of faults for a class of nonlinear systems whose parameters are unknown nonlinear functions of both the measurable operating point and the faults of the system. Neural networks are used to estimate the healthy model's parameters, based on the measurable operating points, when no fault occurs within the system (this procedure is called the training of a healthy system). For this purpose, a modified version of recursive least squares algorithm with normalised signals and an output dead zone are employed. After the training of the healthy system, this recursive algorithm remains on-line to estimate the system parameters which, together with trained neural networkss, are used to recognise, and differentiate, parameter changes which are caused either by the variation of the operating points or by faults.

Full text not available from this repository.

More information

Published date: 1994
Organisations: Southampton Wireless Group


Local EPrints ID: 250221
PURE UUID: 79ad4694-1032-4090-be4d-302c47a121a0

Catalogue record

Date deposited: 04 May 1999
Last modified: 18 Jul 2017 10:43

Export record


Author: H. Wang
Author: M. Brown
Author: C.J. Harris

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.