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

Fault Detection for a Class of Unknown Nonlinear Systems via Associative Memory Networks
Fault Detection for a Class of Unknown Nonlinear Systems via Associative Memory Networks
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.
101--108
Wang, H.
d23f04f1-a300-4744-bd98-2df77c7047df
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Wang, H.
d23f04f1-a300-4744-bd98-2df77c7047df
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Wang, H., Brown, M. and Harris, C.J. (1994) Fault Detection for a Class of Unknown Nonlinear Systems via Associative Memory Networks. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 208 (12), 101--108.

Record type: Article

Abstract

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.

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Published date: 1994
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250221
URI: https://eprints.soton.ac.uk/id/eprint/250221
PURE UUID: 79ad4694-1032-4090-be4d-302c47a121a0

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Date deposited: 04 May 1999
Last modified: 05 Oct 2018 12:11

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