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基于PLS交叉积矩阵非相似度分析的MPC性能监控与诊断

尚林源 田学民 曹玉苹 蔡连芳

尚林源, 田学民, 曹玉苹, 蔡连芳. 基于PLS交叉积矩阵非相似度分析的MPC性能监控与诊断. 自动化学报, 2017, 43(2): 271-279. doi: 10.16383/j.aas.2017.c150782
引用本文: 尚林源, 田学民, 曹玉苹, 蔡连芳. 基于PLS交叉积矩阵非相似度分析的MPC性能监控与诊断. 自动化学报, 2017, 43(2): 271-279. doi: 10.16383/j.aas.2017.c150782
SHANG Lin-Yuan, TIAN Xue-Min, CAO Yu-Ping, CAI Lian-Fang. MPC Performance Monitoring and Diagnosis Based on Dissimilarity Analysis of PLS Cross-product Matrix. ACTA AUTOMATICA SINICA, 2017, 43(2): 271-279. doi: 10.16383/j.aas.2017.c150782
Citation: SHANG Lin-Yuan, TIAN Xue-Min, CAO Yu-Ping, CAI Lian-Fang. MPC Performance Monitoring and Diagnosis Based on Dissimilarity Analysis of PLS Cross-product Matrix. ACTA AUTOMATICA SINICA, 2017, 43(2): 271-279. doi: 10.16383/j.aas.2017.c150782

基于PLS交叉积矩阵非相似度分析的MPC性能监控与诊断

doi: 10.16383/j.aas.2017.c150782
基金项目: 

山东省自然科学基金 ZR2016FQ21

国家自然科学基金 61273160

山东省自然科学基金 ZR2014FL016

中央高校基本科研业务费专项资金 15CX06063A

国家自然科学基金 61403418

详细信息
    作者简介:

    尚林源中国石油大学(华东) 信息与控制工程学院博士研究生.主要研究方向为控制系统性能评价.E-mail:shang_ly2007@163.com

    曹玉苹中国石油大学(华东) 信息与控制工程学院讲师, 博士.主要研究方向为过程故障诊断与预报.E-mail:caoyp@upc.edu.cn

    蔡连芳中国石油大学(华东) 信息与控制工程学院博士.主要研究方向为工业过程故障检测与诊断.E-mail:cailianfang@163.com

    通讯作者:

    田学民中国石油大学(华东) 信息与控制工程学院教授.主要研究方向为过程建模, 先进控制与优化技术, 智能监控与故障诊断.本文通信作者.E-mail:tianxm@upc.edu.cn

MPC Performance Monitoring and Diagnosis Based on Dissimilarity Analysis of PLS Cross-product Matrix

Funds: 

Natural Science Foundation of Shandong Province ZR2016FQ21

National Natural Science Foundation of China 61273160

Natural Science Foundation of Shandong Province ZR2014FL016

the Fundamental Research Funds for the Central Universities 15CX06063A

National Natural Science Foundation of China 61403418

More Information
    Author Bio:

    Ph. D. candidate at the College of Information and Control Engineering, China University of Petroleum. His main research interest is control system performance assessment

    Ph. D., lecturer at the College of Information and Control Engineering, China University of Petroleum. Her research interest covers fault diagnosis and prediction

    Ph. D. at the College of Information and Control Engineering, China University of Petroleum. His research interest covers fault detection and diagnosis in industrial processes

    Corresponding author: TIAN Xue-Min Professor at the College of Information and Control Engineering, China University of Petroleum. His research interest covers modeling, advanced process control and optimization technology, intelligent monitoring, and fault diagnosis. Corresponding author of this paper
  • 摘要: 针对传统基于输出协方差矩阵的性能监控方法未充分考虑过程变量与输出变量之间的相关性问题,提出一种基于偏最小二乘(Partial least squares,PLS)交叉积矩阵非相似度分析的性能监控与诊断方法,用于多变量模型预测控制(Model predictive control,MPC)系统.首先,考虑模型预测控制系统的控制结构,构造包含预测误差的增广过程变量与输出变量相关性的PLS交叉积矩阵,通过非相似度分析方法将交叉积矩阵的非相似度比较转化为转换矩阵特征值的比较.然后提取转换矩阵中表征最大非相似度的l个特征值构造实时性能指标,对MPC系统进行性能监控.检测到性能下降后,进一步利用转换矩阵的特征值诊断性能恶化源.Wood-Berry二元精馏塔上的仿真结果表明,所提方法能够有效地提高监控性能,并准确地定位性能恶化源.
    1)  本文责任编委 钟麦英
  • 图  1  模型预测控制系统内模结构

    Fig.  1  Schematic diagram of the internal model control structure for model predictive control

    图  2  转换矩阵特征值分布示意图

    Fig.  2  Distribution diagram of the eigenvalues of transformed matrixes

    图  3  模型失配时矩阵C与矩阵$\bar { {\varPhi}}$所构成的椭圆

    Fig.  3  Ellipses figure formed by matrices C and $\bar{\varPhi}$ with model mismatch

    图  4  模型失配时的实时监控曲线

    Fig.  4  Real-time monitoring curves of model mismatch

    图  5  干扰特征变化时矩阵C与矩阵$\bar { {\varPhi}}$所构成的椭圆

    Fig.  5  Ellipses figure formed by matrices C and $\bar { {\varPhi}}$ with changing disturbance

    图  6  干扰特征变化时的实时监控曲线

    Fig.  6  Real-time monitoring curves of changing disturbance

    图  7  约束饱和时矩阵C与矩阵$\bar { {\varPhi}}$所构成的椭圆

    Fig.  7  Ellipses figure formed by matrices C and $\bar { {\varPhi}}$ with output constraint saturation

    图  8  输出约束饱和时的实时监控曲线

    Fig.  8  Real-time monitoring curves of output constraint saturation

    图  9  较小模型失配程度下的实时监控曲线

    Fig.  9  Real-time monitoring curves under small extent of model mismatch

    表  1  恶化性能类别及参数设置

    Table  1  Classes and parameters of performance deterioration

    模式库 工况 相应参数 参数变化
    $\rm {CL}_1$ 过程模型失配 首行增益 $(12.8, {\rm{ \ -}}18.9)\to (25.6, {\rm{ }} \ -37.8)$
    $\rm {CL}_2$ 干扰特征变化 标准差 $0.1 \to 0.13$
    $\rm {CL}_3$ 输出约束饱和 输出约束 无$\to [-0.65 \ \ {\rm{ + }}0.65]$
    下载: 导出CSV

    表  2  不同程度模型失配下的性能指标

    Table  2  Performance under different degree of model mismatch

    过程传函矩阵首行增益 $\eta _{\det }$ $\eta_{\rm dissim}$ $\eta_{ r}$
    (20.48, -30.24) 0.9475 0.8547 0.4142
    (23.04, -34.02) 1.0035 0.8374 0.3890
    (25.60, -37.80) 1.0573 0.8217 0.3698
    (28.16, -41.58) 1.1092 0.8075 0.3542
    (30.72, -45.36) 1.1589 0.7948 0.3413
    下载: 导出CSV

    表  3  不同干扰标准差下的性能指标

    Table  3  Performance under different standard deviation of disturbance

    标准差 $\eta _{\det }$ $\eta_{\rm dissim}$ $\eta_{ r}$
    0.11 0.6622 0.8365 0.7308
    0.12 0.4675 0.7538 0.6520
    0.13 0.3394 0.6807 0.5836
    0.14 0.2524 0.6162 0.5242
    0.15 0.1915 0.5593 0.4726
    下载: 导出CSV

    表  4  不同程度输出约束饱和下的性能指标

    Table  4  Performance under different degree of output constraint saturation

    输出约束 $\eta _{\det }$ $\eta_{\rm dissim}$ $\eta_{ r}$
    [-0.75 +0.75] 0.4881 0.4457 0.0879
    [-0.70 +0.70] 0.4879 0.4325 0.0606
    [-0.65 +0.65] 0.4868 0.4248 0.0444
    [-0.60 +0.60] 0.4850 0.4189 0.0340
    [-0.55 +0.55] 0.4850 0.4159 0.0269
    下载: 导出CSV

    表  5  各类测试数据的性能诊断结果

    Table  5  Performance diagnosis results corresponding various test data

    恶化源 参数值 ${{SI}}_{x1}$ ${{SI}}_{x2}$ ${{SI}}_{x3}$
    $\rm {CP}_1$ (20.48 -30.24) 0.9959 0.7029 0.5201
    (23.04 -34.02) 0.9992 0.6886 0.5180
    (25.60 -37.80) 1.0000 0.6776 0.5163
    (28.16 -41.58) 0.9994 0.6685 0.5149
    (30.72 -45.36) 0.9978 0.6609 0.5137
    $\rm {CP}_2$ 0.11 0.7652 0.9725 0.5231
    0.12 0.7217 0.9936 0.5091
    0.13 0.6776 1.0000 0.4941
    0.13 0.6344 0.9945 0.4797
    0.13 0.5928 0.9798 0.4665
    $\rm {CP}_3$ [-0.75 +0.75] 0.5345 0.5649 0.9342
    [-0.70 +0.70] 0.5230 0.5221 0.9857
    [-0.65 +0.65] 0.5163 0.4941 1.0000
    [-0.60 +0.60] 0.5120 0.4751 0.9896
    [-0.55 +0.55] 0.5088 0.4594 0.9648
    下载: 导出CSV
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  • 收稿日期:  2015-11-20
  • 录用日期:  2016-06-06
  • 刊出日期:  2017-02-01

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