东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2011, Vol. 32 ›› Issue (9): 1265-1268+1298.DOI: -

• 论著 • 上一篇    下一篇

炼铁设备故障预测模型的建立与应用

郭宪臻;陈先利;关志民;沈峰满;   

  1. 东北大学材料与冶金学院;安阳钢铁集团公司炼铁厂;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(51074040)

Development and application of equipment malfunction prediction models for ironmaking process

Guo, Xian-Zhen (1); Chen, Xian-Li (2); Guan, Zhi-Min (1); Shen, Feng-Man (1)   

  1. (1) School of Materials and Metallurgy, Northeastern University, Shenyang 110819, China; (2) Ironmaking Plant, Anyang Steel Group Company, Anyang 455004, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Shen, F.-M.
  • About author:-
  • Supported by:
    -

RichHTML

0

PDF (PC)

691

摘要： 炼铁设备运行过程多样复杂,设备故障的预测与分析应适应现代化的设备管理要求.将灰色系统GM(1,1)与新陈代谢模型相结合,建立了炼铁设备故障预测模型.该模型对实时数据进行处理,通过分析数据间的规律,预测设备的可靠性.安钢热风炉风机运行动态监测的实例表明:该模型可实时地根据设备运行状态进行数据分析,与传统的分析手段相比,具有快捷、方便、可信度高的特点.模型可以大幅度减少模拟计算工作量、提高预测精度.

关键词: 炼铁, 设备故障, 灰色系统理论, 预测模型

Abstract: The operation process of ironmaking equipment is varied and complex, so forecasting and analysis of equipment malfunction need to satisfy the modern requirements of equipment management. A malfunction prediction model of ironmaking equipment was established by the combination of gray system GM(1, 1) model and metabolism model. By analyzing the real-time data and finding the law among them, the model can predict the reliability of equipment more precisely. The monitoring of Anyang steel hot blast stove showed that this model can analyse the data timely according to the real time status of equipment. This model can work faster, more convenient and reliable than the traditional method, which can reduce significantly the simulated calculating works and improve the accuracy of prediction.

中图分类号: