基于改进主观贝叶斯方法识别电熔镁炉异常工况

doi:10.12068/j.issn.1005-3026.2021.02.001

东北大学学报（自然科学版） ›› 2021, Vol. 42 ›› Issue (2): 153-159.DOI: 10.12068/j.issn.1005-3026.2021.02.001

• 信息与控制 • 下一篇

基于改进主观贝叶斯方法识别电熔镁炉异常工况

袁杰¹，王姝^1,2，王福利^1,2，孙晓辉³

(1.东北大学信息科学与工程学院，辽宁沈阳110819； 2.东北大学流程工业综合自动化国家重点实验室，辽宁沈阳110819； 3.大连天籁安全风险管理技术有限公司，辽宁大连 116600)

发布日期:2021-03-05
通讯作者: 袁杰
作者简介:袁杰(1987-)，男，山东潍坊人，东北大学博士研究生; 王福利(1957-)，男，辽宁沈阳人，东北大学教授，博士生导师.
基金资助:
国家自然科学基金资助项目(61973057，61903272，61533007)；矿冶过程自动化国家重点实验室开放基金资助项目(BGRIMM-KZSKL-2018-09).

Abnormal Condition Recognition Based on Improved Subjective Bayesian Method for Fused Magnesium Furnace

YUAN Jie¹， WANG Shu^1,2， WANG Fu-li^1,2， SUN Xiao-hui³

1. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China; 2. State Key Laboratory of Synthetical Automation for Process Industries， Northeastern University， Shenyang 110819， China; 3. Tianlai Security Risk Management Technology Co.， Ltd.， Dalian 116600， China.

Published:2021-03-05
Contact: WANG Fu-li
About author:-
Supported by:
-

摘要/Abstract

摘要： 电熔镁炉熔炼过程信息包含大量的不确定性，基于大数据分析的方法难以应用.为准确识别电熔镁炉熔炼过程的异常工况，提出一种基于改进的主观贝叶斯在线规则推理方法.传统的主观贝叶斯方法参数赋值范围过大，针对这一问题，使用映射函数将参数赋值范围缩小到有限区间，以提高方法的实用性.在规则推理时，使用模糊隶属度函数对观察和证据进行模糊匹配，以提高工况识别的鲁棒性和准确率.仿真分析表明该方法可以有效描述规则中的不确定性信息，准确识别电熔镁炉熔炼过程的异常工况.

关键词: 电熔镁炉；异常工况识别；主观贝叶斯；不确定推理；模糊函数

Abstract: It is difficult for big data analysis to be applied to the smelting process of fused magnesium furnace because of a lot of uncertain information of the process. In order to identify abnormal conditions accurately， an online rule reasoning method based on improved subjective Bayesian is proposed. In view of the problem that the parameter value range of the traditional subjective Bayesian method is too wide， the mapping function is used to limit the value range to a finite interval， which improves the practicability of the method. In order to improve the robustness and accuracy of condition recognition， the fuzzy membership function is utilized to match the observation and evidence in the reasoning. Simulation results show that the method can effectively describe the uncertain information in the rules and accurately identify the abnormal conditions in the smelting process of fused magnesium furnace.

Key words: fused magnesium furnace; abnormal condition recognition; subjective Bayesian; uncertain reasoning; fuzzy function

中图分类号:

TP249

袁杰，王姝，王福利，孙晓辉. 基于改进主观贝叶斯方法识别电熔镁炉异常工况[J]. 东北大学学报（自然科学版）, 2021, 42(2): 153-159.

YUAN Jie， WANG Shu， WANG Fu-li， SUN Xiao-hui. Abnormal Condition Recognition Based on Improved Subjective Bayesian Method for Fused Magnesium Furnace[J]. Journal of Northeastern University(Natural Science), 2021, 42(2): 153-159.

参考文献

[1]Wu Z W， Liu T F， Jiang Z P，et al.Nonlinear control tools for fused magnesium furnaces: design and implementation［J］.IEEE Transactions on Industrial Electronics，2018， 65 (9):7248-7257.
[2]李荟，王福利，李鸿儒.电熔镁炉熔炼过程异常工况识别及自愈控制方法［J］.自动化学报，2020，46(7): 1411-1419.(Li Hui， Wang Fu-li， Li Hong-ru.Abnormal condition identification and self-healing control scheme for the electro-fused magnesia smelting process［J］.Acta Automatica Sinica，2020， 46(7):1411-1419.)
[3]Ding S Y， Wang Z J， Kong W J，et al.Electrode regulating system modeling in electrical smelting furnace using recurrent neural network with attention mechanism［J］.Neurocomputing，2019， 359:32-40.
[4]卢绍文，王克栋，吴志伟，等.基于深度卷积网络的电熔镁炉欠烧工况在线识别［J］.控制与决策， 2019， 34(7):1537-1544.(Lu Shao-wen， Wang Ke-dong， Wu Zhi-wei，et al.Online detection of semi-molten of fused magnesium furnace based on deep convolutional neural network［J］.Control and Decision，2019， 34(7):1537-1544.)
[5]吴高昌，刘强，柴天佑，等.基于时序图像深度学习的电熔镁炉异常工况诊断［J］.自动化学报， 2019， 45(8):1475-1485.(Wu Gao-chang， Liu Qiang， Chai Tian-you，et al.Abnormal condition diagnosis through deep learning of image sequences for fused magnesium furnaces［J］.Acta Automatica Sinica，2019， 45(8):1475-1485.)
[6]李鸿儒，王奕文，邓靖川.基于信息融合的电熔镁炉熔炼异常工况等级识别［J］.东北大学学报(自然科学版)， 2020， 41(2):153-157.(Li Hong-ru， Wang Yi-wen， Deng Jing-chuan.Information fusion based abnormal condition levels recognition of smelting in fused magnesium furnace［J］.Journal of Northeastern University(Natural Science)，2020， 41(2):153-157.)
[7]Coulibaly L， Kamsu-Foguem B， Tangara F.Rule-based machine learning for knowledge discovering in weather data［J］.Future Generation Computer Systems，2020， 108: 861-878.
[8]Vera J C D， Ortiz G M N， Molina C，et al.Extending knowledge based redundancy in association rules with imprecise knowledge［J］.IEEE Latin America Transactions，2019， 17(4):648-653.
[9]Xie T， Gong Z T.A hesitant soft fuzzy rough set and its applications［J］.IEEE Access， 2019， 7: 167766-167783.
[10]Zhao G Z， Chen A G， Lu G X，et al.Data fusion algorithm based on fuzzy sets and D-S theory of evidence［J］.Tsinghua Science and Technology，2020， 25(1):12-19.
[11]Li L， Yue W C.Dynamic uncertain causality graph based on intuitionistic fuzzy sets and its application to root cause analysis［J］.Applied Intelligence，2020， 50(1):241-255.
[12]Gnen M， Johnson W O， Lu Y G，et al.Comparing objective and subjective Bayes factors for the two-sample comparison: the classification theorem in action［J］.The American Statistician，2019， 73(1):22-31.
[13]Uzunolu B.An adaptive Bayesian approach with subjective logic reliability networks for preventive maintenance［J］.IEEE Transactions on Reliability，2020， 69(3):916-924.中文.(英文)

基于改进主观贝叶斯方法识别电熔镁炉异常工况

Abnormal Condition Recognition Based on Improved Subjective Bayesian Method for Fused Magnesium Furnace

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

本文评价

[1]	李小彭，尚东阳，李凡杰，闻邦椿. 输电线巡检机器人位姿变化的柔性关节控制策略[J]. 东北大学学报:自然科学版, 2020, 41(11): 1577-1583.
[2]	李小彭，李凯，樊星，张凌越. 双臂巡检机器人位姿变化下沿悬链线行走能力分析[J]. 东北大学学报（自然科学版）, 2022, 43(6): 872-880.