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基于稀疏残差距离的多工况过程故障检测方法研究

郭小萍 刘诗洋 李元

郭小萍, 刘诗洋, 李元. 基于稀疏残差距离的多工况过程故障检测方法研究. 自动化学报, 2019, 45(3): 617-625. doi: 10.16383/j.aas.c170389
引用本文: 郭小萍, 刘诗洋, 李元. 基于稀疏残差距离的多工况过程故障检测方法研究. 自动化学报, 2019, 45(3): 617-625. doi: 10.16383/j.aas.c170389
GUO Xiao-Ping, LIU Shi-Yang, LI Yuan. Fault Detection of Multi-mode Processes Employing Sparse Residual Distance. ACTA AUTOMATICA SINICA, 2019, 45(3): 617-625. doi: 10.16383/j.aas.c170389
Citation: GUO Xiao-Ping, LIU Shi-Yang, LI Yuan. Fault Detection of Multi-mode Processes Employing Sparse Residual Distance. ACTA AUTOMATICA SINICA, 2019, 45(3): 617-625. doi: 10.16383/j.aas.c170389

基于稀疏残差距离的多工况过程故障检测方法研究

doi: 10.16383/j.aas.c170389
基金项目: 

辽宁省教育厅重点实验室项目 LZ2015059

辽宁省自然科学基金 201602584

国家自然科学基金 61673279

辽宁省教育厅项目 L2015432

国家自然科学基金 61490701

辽宁省教育厅项目 L2016007

详细信息
    作者简介:

    郭小萍    沈阳化工大学信息工程学院副教授.2006年获得东北大学博士学位.主要研究方向为基于数据驱动技术的复杂过程故障检测与诊断.E-mail:gxp2001@sina.com

    刘诗洋    沈阳化工大学信息工程学院硕士研究生.主要研究方向为基于数据驱动的多工况过程故障检测.E-mail:liusy2018@sina.com

    通讯作者:

    李元    沈阳化工大学信息工程学院教授.2005年获得东北大学博士学位.主要研究方向为基于数据驱动技术的复杂过程故障检测与诊断.本文通信作者.E-mail:li-yuan@mail.tsinghua.edu.cn

Fault Detection of Multi-mode Processes Employing Sparse Residual Distance

Funds: 

Department of Education Key Laboratory Project of Liaoning Province LZ2015059

Natural Science Foundation of Liaoning Province 201602584

National Natural Science Foundation of China 61673279

Foundation of Liaoning Educational Committee L2015432

National Natural Science Foundation of China 61490701

Foundation of Liaoning Educational Committee L2016007

More Information
    Author Bio:

    Associate professor at the Institute of information engineering, Shenyang University of Chemical Engineering. She received her Ph. D. degree from Northeastern University in 2006. Her research interest covers data-driven based techniques for complex process fault detection and diagnosis

    Master student at the Institute of Information Engineering, Shenyang University of Chemical Engineering. Her research interest covers data-driven based techniques for multiple operating conditions fault detection

    Corresponding author: LI Yuan Professor at the Institute of information engineering, Shenyang University of Chemical Engineering. She received her Ph. D. degree from Northeastern University in 2005. Her research interest covers data-driven based techniques for complex process fault detection and diagnosis. Corresponding author of this paper
  • 摘要: 针对多工况过程,本文提出一种新的基于稀疏残差距离(Sparse residual distance,SRD)统计指标的故障检测方法.首先对正常的多工况标准化后数据直接进行稀疏分解,提取多个工况数据间相关关系,得到字典和对应的稀疏编码,以便构建全局检测模型,避免分工况且突出数据特征.然后计算正常多工况数据的近似值,构建稀疏残差空间,提出计算稀疏残差k近邻距离构建故障检测统计量,利用k近邻捕捉过程具有的非线性、多工况特征.最后通过数值案例和TE(Tennessee Eastman)生产过程进行仿真实验,验证了所提方法的有效性.
    1)  本文责任编委 王伟
  • 图  1  过程检测模型建立流程图

    Fig.  1  Flow chart of establishing process detection model

    图  2  数值投影图

    Fig.  2  Numerical data projection

    图  3  SRD故障检测结果

    Fig.  3  Fault detection results by SRD

    图  4  TE过程工艺流程图

    Fig.  4  TE process flow chart

    图  5  数据投影图

    Fig.  5  Data projection

    图  6  故障6故障检测结果

    Fig.  6  Fault 6 fault detection results

    图  7  故障13故障检测结果

    Fig.  7  Fault 13 fault detection results

    表  1  TE过程故障

    Table  1  Failures of TE process

    故障编号 性质描述 变化类型
    IDV 1 物料A/C进料比改变, 物料B含量不变 阶跃
    IDV 2 物料A/C进料比不变, 物料B含量改变 阶跃
    IDV 4 反应器冷却入口温度改变 阶跃
    IDV 6 物料A进料损失 阶跃
    IDV 7 物料C压力损失 阶跃
    IDV 13 反应动力学参数改变 慢偏移
    IDV 16 未知 未知
    下载: 导出CSV

    表  2  本文采用的TE过程生产模式

    Table  2  TE process production model used in this paper

    生产模式 G/H比率 产品生产率
    1 50/50 7 038 kgh-1 G和7 038 kgh-1 H
    3 90/10 1 000 kgh-1 G和1 111 kgh-1 H
    "kgh-1 G"表示"每小时生产多少千克的G产品", "kgh-1 H"表示"每小时生产多少千克的H产品".
    下载: 导出CSV

    表  3  误报率及检测率汇总表

    Table  3  False alarm rate and detection rate summary table

    故障号 KSVD-R SRD
    误报率(%) 检测率(%) 误报率(%) 检测率(%)
    1 4.50 0.10 100 100 2.00 0 100 100
    2 4.50 0.10 100 98.6 2.00 0 100 100
    4 4.50 0.10 100 100 2.00 0 100 100
    6 4.50 0.10 100 100 2.00 0 100 100
    7 4.50 0.10 100 100 2.00 0 100 100
    13 4.50 0.10 89.6 82.1 2.00 0 97.8 90.2
    16 4.50 0.10 94.6 90.2 2.00 0 98.6 94.7
    注1:表 3中误报率和漏报率下属两列数据, 靠前的一列为控制限为95%的数值, 靠后的一列为控制限为99%的数值
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-07-13
  • 录用日期:  2018-02-07
  • 刊出日期:  2019-03-20

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