Enhanced neural network modelling for process fault diagnosis

被引：0

作者：

Chang, TK ^{[1
]}

Yu, DL ^{[1
]}

Williams, D ^{[1
]}

机构：

[1] Liverpool John Moores Univ, Control Syst Res Grp, Liverpool L3 5UX, Merseyside, England

来源：

CONTROL APPLICATIONS IN MARINE SYSTEMS 2001 (CAMS 2001) | 2002年

关键词：

fault detection; fault isolation; neural networks; non-linear systems; process identification;

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 neural network (NN) based fault detection and isolation (FDI) approach for unknown non-linear system is proposed. This FDI scheme is able to detect both actuator and sensor faults. An enhanced parallel (independent) NN model is trained to represent the process, and used to generate residual. A mean-weight strategy is introduced to overcome the unmodelled noise and disturbance problem. An information pre-processor is implemented to convert the quantitative residual to qualitative form and applied to a NN fault classifier to isolate different faults. The developed techniques are demonstrated with a multi-variable non-linear tank process. Copyright (C) 2001 IFAC.

引用

页码：215 / 220

页数：6

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