Knowledge-elicitation and data-mining: Fusing human and industrial plant information

被引:5
作者
Browne, W [1 ]
Yao, L
Postlethwaite, I
Lowes, S
Mar, M
机构
[1] Univ Reading, Dept Cybernet, Reading RG6 6AY, Berks, England
[2] Univ Leicester, Dept Engn, Leicester LE1 7RH, Leics, England
[3] Alcoa Europe, Flat Rolled Prod, Birmingham B33 9QR, W Midlands, England
基金
英国工程与自然科学研究理事会;
关键词
fusing data-mining methods; metal rolling;
D O I
10.1016/j.engappai.2005.09.005
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process. (C) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:345 / 359
页数:15
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