Design of fault detection for a hydraulic looper using dynamic neural networks

被引：30

作者：

Marcu, Teodor ^{[2
]}

Koeppen-Seliger, Birgit ^{[1
]}

Stuecher, Reinhard ^{[3
]}

机构：

[1] Univ Duisburg Essen, Inst Automat Control & Complex Syst AKS, D-47048 Duisburg, Germany

[2] IAV GmbH, D-38518 Gifhorn, Germany

[3] SMS Demag AG, D-57271 Hilchenbach, Germany

来源：

CONTROL ENGINEERING PRACTICE | 2008年 / 16卷 / 02期

关键词：

fault diagnosis; hydraulic looper; benchmark example; dynamic functional-link neural networks; non-linear system identification; decision logic;

D O I：

10.1016/j.conengprac.2006.11.017

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The contribution investigates the design of a fault detection system for a hydraulic looper from a hot rolling mill plant. A genetically evolved dynamic functional-link neural network is used to identify different relationships between process variables. One step ahead prediction errors, i.e. residuals, are then evaluated by a decision logic using threshold values and different patterns of residual's change in cases of known faulty behaviours. Experimental results are included referring to the separate processing of simulation and real data. The obtained results characterise the efficiency of the presented approach as depending on the availability of signals from the automation system of the considered process. (c) 2007 Elsevier Ltd. All rights reserved.

引用

页码：192 / 213

页数：22

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