Dynamic system fault diagnosis based on neural network modelling

被引：0

作者：

Zhou, J ^{[1
]}

Bennett, S ^{[1
]}

机构：

[1] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England

来源：

(SAFEPROCESS'97): FAULT DETECTION, SUPERVISION AND SAFETY FOR TECHNICAL PROCESSES 1997, VOLS 1-3 | 1998年

关键词：

fault diagnosis; neural-networks models; gas turbines; nonlinear systems; prediction methods;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A fault diagnosis scheme for nonlinear unknown systems comprising two stages: residual generation and residual evaluation is presented. The essential step to this goal is to build up a bank of recurrent neural network models that perform long-term predictions. This scheme has advantages over conventional FDD schemes in that it can deal not only with the nonlinearity presented in the system, but it also provides useful information about the deterioration level of system. An application of the scheme for detection and identification of faults occurring in a gas turbine engine is presented. Simulation studies show the effectiveness of proposed scheme. Copyright (C) 1998 IFAC.

引用

页码：55 / 60

页数：6

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