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基于多级动态主元分析的电熔镁炉异常工况诊断

刘强 孔德志 郎自强

刘强, 孔德志, 郎自强. 基于多级动态主元分析的电熔镁炉异常工况诊断. 自动化学报, 2021, 47(11): 2570−2577 doi: 10.16383/j.aas.c190313
引用本文: 刘强, 孔德志, 郎自强. 基于多级动态主元分析的电熔镁炉异常工况诊断. 自动化学报, 2021, 47(11): 2570−2577 doi: 10.16383/j.aas.c190313
Liu Qiang, Kong De-Zhi, Lang Zi-Qiang. Multi-level dynamic principal component analysis for abnormality diagnosis of fused magnesia furnaces. Acta Automatica Sinica, 2021, 47(11): 2570−2577 doi: 10.16383/j.aas.c190313
Citation: Liu Qiang, Kong De-Zhi, Lang Zi-Qiang. Multi-level dynamic principal component analysis for abnormality diagnosis of fused magnesia furnaces. Acta Automatica Sinica, 2021, 47(11): 2570−2577 doi: 10.16383/j.aas.c190313

基于多级动态主元分析的电熔镁炉异常工况诊断

doi: 10.16383/j.aas.c190313
基金项目: 国家自然科学基金(61991401, U20A20189, 61673097, 61833004), 兴辽英才计划项目(XLYC1907049, XLYC1808001), 中央高校基本科研业务费(N180802004)资助
详细信息
    作者简介:

    刘强:东北大学教授. 主要研究方向为数据驱动的建模, 过程监控与故障诊断. 本文通信作者.E-mail: liuq@mail.neu.edu.cn

    孔德志:东北大学流程工业综合自动化国家重点实验室硕士研究生. 主要研究方向为数据驱动建模与故障诊断.E-mail: 1770563@stu.neu.edu.cn

    郎自强:英国谢菲尔德大学自动控制与系统工程系教授. 主要研究方向为非线性系统建模、分析、设计和信号处理理论及工程应用.E-mail: z.lang@sheffield.ac.uk

Multi-level Dynamic Principal Component Analysis for Abnormality Diagnosis of Fused Magnesia Furnaces

Funds: Supported by the National Natural Science Foundation of China (61991401, U20A20189, 61673097, 61833004), LiaoNing Revitalization Talents Program (XLYC1907049, XLYC1808001), the Fundamental Research Funds for the Central Universities (N180802004)
More Information
    Author Bio:

    LIU Qiang Professor at Northeastern University, China. His research interest covers data driven modeling, statistical process monitoring, fault diagnosis of complex industrial processes. Corresponding author of this paper

    KONG De-Zhi Master student at the State Key Laboratory of Synthetical Automation for process Industries, Northeastern University. His research interst covers data-driven modeling and fault diagnosis

    LANG Zi-Qiang Chair professor of Complex Systems Analysis and Design in the Department of Automatic Control and Systems Engineering at the University of Sheffield, UK. His research interest covers nonlinear system modelling, analysis, design and signal processing as well as their engineering applications

  • 摘要: 电熔镁熔炼过程中的异常工况(如半熔化工况)直接影响产品质量、威胁人员和生产安全, 有必要及时诊断. 但与异常直接相关的超高温熔池温度(>2850 ℃)难以利用温度传感器检测, 目前现场主要依靠工人在定期巡检时人眼观察炉壁来诊断, 工作强度大、安全度低、诊断不及时. 针对上述问题, 本文提出一种炉体动态图像驱动的电熔镁炉异常工况实时诊断方法. 结合电熔镁炉熔炼各区域温度分布的空间特征、正常工况下熔炼温度变化和水雾扰动引入的图像时序特征、以及异常工况下温度异常区域持续发亮扩大的特征, 在对炉体动态图像进行空间多级划分的基础上, 提出了一种多级动态主元分析(Multi-level dynamic principal component analysis, MLDPCA) 动态图像分块建模方法. 在此基础上, 提出基于MLDPCA的逐级诊断方法与基于贡献图的异常定位方法. 最后, 采用某电熔镁生产现场的实际图像进行方法验证, 结果表明了所提方法的有效性.
    1)  收稿日期 2019-04-22    录用日期 2019-07-30 Manuscript received April 22, 2019; accepted July 30, 2019 国家自然科学基金(61991401, U20A20189, 61673097, 61833004), 兴辽英才计划项目(XLYC1907049, XLYC1808001), 中央高校基本科研业务费(N180802004)资助 Supported by National Natural Science Foundation of China (61991401, U20A20189, 61673097, 61833004), LiaoNing Revitalization Talents Program (XLYC1907049, XLYC1808001), the Fundamental Research Funds for the Central Universities (N180802004) 本文责任编委 杨浩 Recommended by Associate Editor YANG Hao 1. 东北大学流程工业综合自动化国家重点实验室 沈阳 110819 中国    2. 英国谢菲尔德大学自动控制和系统工程系 谢菲尔德市S1 3JD 英国 1. State Key Laboratory of Synthetical Automation for Process
    2)  Industries, Northeastern University, Shenyang 110819, China 2. Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK
  • 图  1  电熔镁生产过程

    Fig.  1  Fused magnesia manufacturing processes

    图  2  (a)正常工况图像; (b)半熔化工况图像

    Fig.  2  (a) Image of normal situation; (b) Image of semimolten situation

    图  3  时序图像序列分级结构

    Fig.  3  Hierarchical structure of time series image

    图  4  基于MLDPCA的异常工况诊断流程图

    Fig.  4  Flow chart of MLDPCA based abnormal situation diagnosis

    图  5  (a) MLDPCA诊断结果; (b)多级PCA诊断结果

    Fig.  5  (a) Diagnosis result of MLDPCA; (b) Diagnosis result of MLPCA

    图  6  每个子块的监控指标

    Fig.  6  Monitoring index for each sub-block

    图  7  (a) 第540帧时第9块贡献图; (b) 第700帧时第5块贡献图

    Fig.  7  (a) Contribution plot in 9th block at 540th; (b) Contribution plot in 5th block at 700th

    图  8  (a) 第540帧时炉壁图像; (b) 第700帧时炉壁图像

    Fig.  8  (a) Image at 540th frame; (b) Image at 700th frame

    表  1  电熔镁炉半熔化工况诊断误报率

    Table  1  False positive rates of semimolten for FMF

    诊断方法误报率 (不加时间延迟诊断)误报率 (加时间延迟诊断)
    多级 PCA35.17 %8.69 %
    本文方法7.63 %0.1 %
    下载: 导出CSV

    表  2  建模时间与诊断时间

    Table  2  Cost time of modeling and online diagnosis

    诊断方法建模时间 (秒)诊断时间 (秒)
    多级 PCA54.950.87
    本文方法145.140.94
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-04-22
  • 录用日期:  2019-07-30
  • 网络出版日期:  2021-09-20
  • 刊出日期:  2021-11-18

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