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面向全量测点耦合结构分析与估计的工业过程监测方法

赵健程 赵春晖

赵健程, 赵春晖. 面向全量测点耦合结构分析与估计的工业过程监测方法. 自动化学报, 2022, 48(x): 1−21 doi: 10.16383/j.aas.c220090
引用本文: 赵健程, 赵春晖. 面向全量测点耦合结构分析与估计的工业过程监测方法. 自动化学报, 2022, 48(x): 1−21 doi: 10.16383/j.aas.c220090
Zhao Jian-Cheng, Zhao Chun-Hui. An industrial process monitoring method based on total measurement point coupling structure analysis and estimation. Acta Automatica Sinica, 2022, 48(x): 1−21 doi: 10.16383/j.aas.c220090
Citation: Zhao Jian-Cheng, Zhao Chun-Hui. An industrial process monitoring method based on total measurement point coupling structure analysis and estimation. Acta Automatica Sinica, 2022, 48(x): 1−21 doi: 10.16383/j.aas.c220090

面向全量测点耦合结构分析与估计的工业过程监测方法

doi: 10.16383/j.aas.c220090
基金项目: 国家自然科学基金杰出青年基金 (62125306), 工业控制技术国家重点实验室自主课题 (ICT2021A15), 中央高校基本科研业务费专项资金资助(浙江大学NGICS大平台)资助
详细信息
    作者简介:

    赵健程:浙江大学控制科学与工程学院博士研究生. 2021年获得浙江大学控制科学与工程学院自动化专业学士学位. 主要研究方向为大数据分析, 深度学习和零样本学习. E-mail: zhaojiancheng@zju.edu.cn

    赵春晖:浙江大学控制科学与工程学院教授. 2003年获得中国东北大学自动化专业学士学位, 2009年获得中国东北大学控制理论与控制工程专业博士学位, 先后在中国香港科技大学、美国加州大学圣塔芭芭拉分校做博士后研究工作. 主要研究方向为机器学习, 工业大数据解析与应用, 包括化工, 能源以及医疗领域. 本文通信作者. E-mail: chhzhao@zju.edu.cn

An Industrial Process Monitoring Method Based on Total Measurement Point Coupling Structure Analysis and Estimation

Funds: Supported by National Natural Science Foundation of China (62125306), the Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (ICT2021A15), and the Fundamental Research Funds for the Central Universities (Zhejiang University NGICS Platform)
More Information
    Author Bio:

    ZHAO Jian-Cheng Ph.D. candidate at the College of Control Science and Engineering, Zhejiang University. He received the B.Eng. degree from the College of Control Science and Engineering, Zhejiang University in 2021. His research interest covers big data analysis, deep learning, and zero-shot learning

    ZHAO Chun-Hui Professor at the College of Control Science and Engineering, Zhejiang University. She received her bachelor, master, and Ph.D. degrees from the Department of Automation, Northeastern University, Shenyang, China in 2003, 2006, and 2009, respectively. She was a postdoctoral fellow (January 2009-January 2012) at the Hong Kong University of Science and Technology, China and the University of California, Santa Barbara, Los Angeles, CA, USA. Her research interest covers machine learning, analytics of industrial big data, and their applications in energy and medical fields. Corresponding author of this paper

  • 摘要: 实际工业场景中, 需要在生产过程中收集大量测点的数据, 从而掌握生产过程运行状态. 传统的过程监测方法通常仅评估运行状态整体的异常与否, 或对运行状态进行分级评估, 这种方式并不会直接定位故障部位, 不利于故障的高效检修. 为此, 提出了一种基于全量测点估计的监测模型, 根据全量测点估计值与实际值的偏差定义监测指标, 从而实现全量测点的分别精准监测. 为了克服原有的基于工况估计的监测方法监测不全面且对测点间耦合关系建模不充分的问题, 提出了多核图卷积网络(Multi-kernel graph convolution network, MKGCN), 通过将全量传感器测点视为一张全量测点图, 显式地对测点间耦合关系进行建模, 从而实现了全量传感器测点的同步工况估计. 此外, 面向在线监测场景, 设计了基于特征逼近的自迭代方法, 从而克服了在异常情况下由于测点间强耦合导致的部分测点估计值异常的问题. 所提出的方法在电厂百万千瓦超超临界机组中引风机的实际数据上进行了验证, 结果显示, 提出的监测方法与其他典型方法相比能够更精准地检测出发生故障的测点.
  • 图  1  LSTM内部结构

    Fig.  1  Internal structure of LSTM

    图  2  面向全量测点估计的多核图卷积模型结构

    Fig.  2  Structure of multi-kernel graph convolution model for total measurement points estimation

    图  3  MKGCN层的计算过程

    Fig.  3  Calculation process of MKGCN layer

    图  4  MKGCN层的堆叠使用

    Fig.  4  The stacking use of MKGCN layers

    图  5  自迭代方法

    Fig.  5  Self-iterative method

    图  6  训练数据中测点间相关性

    Fig.  6  Correlation between measuring points on modeling data

    图  7  基于MKGCN的模型监测效果图($ \text{var} \in F$)

    Fig.  7  Monitoring diagram of model based on MKGCN($ \text{var} \in F$)

    图  8  MEST方法漏报的部分异常变量

    Fig.  8  Some abnormal variables partially missed by MEST method

    图  9  AE模型误报的部分正常变量

    Fig.  9  Some normal variables with serious false alarm(AE)

    图  10  不同通道的邻接核可视化结果

    Fig.  10  Visualization results of adjacency kernels in different channels

    图  11  测点12, 测点25的估计值对比

    Fig.  11  Comparison of working condition estimated values of measuring point 12 and measuring point 25

    表  1  引风机测点对应表

    Table  1  Measuring points of induced draft fan

    0功率信号三选值11引风机水平振动22引风机油箱温度
    1进气温度12引风机后轴承温度123引风机中轴承温度1
    2引风机电机定子线圈温度113引风机后轴承温度224引风机中轴承温度2
    3引风机电机定子线圈温度214引风机后轴承温度325引风机中轴承温度3
    4引风机电机定子线圈温度315引风机键相26炉膛压力
    5引风机电机水平振动116引风机静叶位置反馈27引风机出口风温
    6引风机电机水平振动217引风机前轴承温度128引风机入口压力
    7引风机电机轴承温度118引风机前轴承温度229引风机出口风压
    8引风机电机轴承温度219引风机前轴承温度330引风机静叶开度指令
    9引风机电流20引风机润滑油温度31总燃料量
    10引风机风垂直振动21引风机润滑油压力32炉膛压力
    下载: 导出CSV

    表  2  基于MKGCN层的估计模型的结构

    Table  2  Structure of working condition estimation model based on MKGCN layer

    序号网络层数目参数激活函数
    1BiLSTM$n$$[{ {\rm{input}}\_{\rm{size}}} = len, {{\rm{hidden}}\_{\rm{size}}} = ld]$None
    FC$n$$[{ {\rm{input}}\_{\rm{size}}} = len, {{\rm{output}}\_{\rm{size}}} = 2 \times ld]$
    2MKGCN$1$$[ {{c_{{\rm{in}}}} = 1,n{o_{{\rm{in}}}} = n,f{e_{{\rm{in}}}} = 2 \times ld} $Tanh
    $ {{c_{{\rm{out}}}} = oc,n{o_{{\rm{out}}}} = n,f{e_{{\rm{out}}}} = 4 \times ld}] $
    3FC 0$n$$[{ {\rm{input}}\_{\rm{size}}} = 4 \times ld, {{\rm{output}}\_{\rm{size}}} = 2 \times ld]$Tanh
    4FC 1$n$$[{ {\rm{input}}\_{\rm{size}}} = 2 \times ld, {{\rm{output}}\_{\rm{size}}} = 1]$Tanh
    5FC 2$n$$[{ {\rm{input}}\_{\rm{size}}} = \;oc, {{\rm{output}}\_{\rm{size}}} = 1]$None
    特征逼近层(FC)$n$$[{ {\rm{input}}\_{\rm{size}}} = oc, {{\rm{output}}\_{\rm{size}}} = 1]$None
    下载: 导出CSV

    表  3  基于GCN的估计模型结构

    Table  3  Structure of working condition estimation model based on GCN

    序号网络层数目参数激活函数
    1BiLSTM$n$$[{ {\rm{input}}\_{\rm{size}}} = len, {{\rm{hidden}}\_{\rm{size}}} = ld]$None
    2GCN1$[{\rm{in}}\_{\rm{feature}} = 2 \times ld, {\rm{out}}\_{\rm{feature}} = 4 \times ld]$Tanh
    3FC 0$n$$[{ {\rm{input}}\_{\rm{size}}} = 4 \times ld, {{\rm{output}}\_{\rm{size}}} = 2 \times ld]$Tanh
    4FC 1$n$$[{ {\rm{input}}\_{\rm{size}}} = 2 \times ld, {{\rm{output}}\_{\rm{size}}} = ld]$Tanh
    5FC 2$n$$[{ {\rm{input}}\_{\rm{size}}} = ld, {{\rm{output}}\_{\rm{size}}} = 1]$None
    下载: 导出CSV

    表  4  模型实现和参数网格搜索范围

    Table  4  Model implementation and parameter grid search range

    方法Python包超参数超参数调整范围
    PLSRscikit-learn$nc$$nc = \left\{ {5,10,15,20,25} \right\}$
    ELMD.C. Lambert$E,\alpha $$ E = \left\{ {50,100,150,200,250} \right\}, $
    $ \alpha = \left\{ {0.1,0.3,0.5,0.7,0.9} \right\} $
    FCPaddlePaddle$ld$$ld = \left\{ {8,16,32,64,128} \right\}$
    BiLSTMPaddlePaddle$ld$$ld = \left\{ {8,16,32,64,128} \right\}$
    Conv1dPaddlePaddle$ld$$ld = \left\{ {8,16,32,64,128} \right\}$
    GCNPaddlePaddle$ld$$ld = \left\{ {8,16,32,64,128} \right\}$
    MKGCNPaddlePaddle$ld,oc$$ ld = \left\{ {8,16,32,64,128} \right\},$
    $ oc = \left\{ {2,4,8,16,32} \right\} $
    下载: 导出CSV

    表  5  网格搜索结果与深度神经网络方法在最优超参数下总参数量

    Table  5  Grid search results and total parameters of depth neural network method with optimal hyperparameters

    方法最优超参数模型数总参数量
    PLSR$nc = 15$$n$$/$
    ELMR$E = 200,\alpha = 0.9$$n$$/$
    MEST$/$$1$$/$
    FC$ld = 128$$n$$5 \times {10^5}$
    BiLSTM$ld = 128$$n$$6.9 \times {10^6}$
    Conv1d$ld = 128$ $n$$9 \times {10^5}$
    GCN$ld = 64$$1$$9.8 \times {10^6}$
    MKGCN$ld = 8,oc = 32$$1$$1.8 \times {10^5}$
    下载: 导出CSV

    表  6  测试数据上不同模型的估计结果(RMSE)

    Table  6  Results of different working condition estimation models on test data (RMSE)

    变量PLSRELMFCBiLSTMConv1dGCNMESTMKGCN
    $\text{var} \in N$0.0420.0640.0590.0520.0600.0420.0050.044
    $\text{var} \in F$0.0460.0760.0590.0490.0820.0490.0060.046
    下载: 导出CSV

    表  7  测试数据上不同模型的估计结果(MAE)

    Table  7  Results of different working condition estimation models on test data (MAE)

    变量PLSRELMFCBiLSTMConv1dGCNMESTMKGCN
    $\text{var} \in N$0.0340.0520.0490.0430.0510.0340.0040.036
    $\text{var} \in F$0.0390.0660.0500.0410.0700.0430.0050.039
    下载: 导出CSV

    表  8  监测数据上各监测指标($ \text{var} \in N$)

    Table  8  Monitoring indicators on monitoring data($ \text{var} \in N$)

    指标PLSRELMFCBiLSTMConv1dGCNMESTMKGCN
    $\text{False}_\text{p}$13.26729.57334.26727.39242.58123.5682.8534.500
    $\text{False}_\text{n}$00000000
    F192.89582.64879.32484.13172.95186.64298.55397.698
    下载: 导出CSV

    表  9  监测数据上各监测指标($ \text{var} \in F$)

    Table  9  Monitoring indicators on monitoring data($ \text{var} \in F$)

    指标PLSRELMFCBiLSTMConv1dGCNMESTMKGCN
    $\text{False}_\text{p}$15.95830.58331.37532.39037.16210.769010.769
    $\text{False}_\text{n}$24.2505.0425.9171.1401.7746.96833.2081.056
    F179.68180.20379.36280.30276.64491.09280.09093.836
    下载: 导出CSV

    表  10  基于AE的估计模型的结构

    Table  10  Structure of working condition estimation model based on AE

    序号网络层数目参数激活函数
    1BiLSTM1$[{ {\rm{input}}\_{\rm{size}}} = \;len,{{\rm{hidden}}\_{\rm{size}}} = 2 \times ld]$None
    2FC 01$[{ {\rm{input}}\_{\rm{size}}} = 4 \times ld,{{\rm{output}}\_{\rm{size}}} = 2 \times ld]$Tanh
    3FC 11$[{ {\rm{input}}\_{\rm{size}}} = 2 \times ld,{{\rm{output}}\_{\rm{size}}} = ld]$Tanh
    4FC 21$[{ {\rm{input}}\_{\rm{size}}} = ld,{{\rm{output}}\_{\rm{size}}} = 2 \times ld]$Tanh
    5FC 31$[{ {\rm{input}}\_{\rm{size}}} = 2 \times ld,{{\rm{output}}\_{\rm{size}}} = n]$None
    下载: 导出CSV

    表  11  AE与MKGCN实验结果对比(MKGCN实验结果同表6 ~ 表9)

    Table  11  Comparison of experimental results between AE and MKGCN (The experimental results of MKGCN are the same as Table 6 ~ Table 9)

    指标AEMKGCN
    RMSE, $\text{var} \in N$0.0200.044
    RMSE, $\text{var} \in F$0.0220.046
    MAE, $\text{var} \in N$0.0160.036
    MAE, $\text{var} \in F$0.0190.039
    $\text{False}_\text{p}$, $\text{var} \in N$38.8114.500
    $\text{False}_\text{n}$, $\text{var} \in N$00
    F1, $\text{var} \in N$75.92297.698
    $\text{False}_\text{p}$, $\text{var} \in F$35.00910.769
    $\text{False}_\text{n}$, $\text{var} \in F$0.8871.056
    F1, $\text{var} \in F$78.50593.836
    下载: 导出CSV

    表  12  单输出通道与多输出通道性能对比

    Table  12  Performance comparison between single output channel and multiple output channels

    指标$oc = 1$$oc = 32$
    RMSE, $\text{var} \in N$0.0430.044
    RMSE, $\text{var} \in F$0.0550.046
    MAE, $\text{var} \in N$0.0350.036
    MAE, $\text{var} \in F$0.0490.039
    $\text{False}_\text{p}$, $\text{var} \in N$38.4864.500
    $\text{False}_\text{n}$, $\text{var} \in N$00
    F1, $\text{var} \in N$76.17297.698
    $\text{False}_\text{p}$, $\text{var} \in F$36.02210.769
    $\text{False}_\text{n}$, $\text{var} \in F$5.2371.056
    F1, $\text{var} \in F$76.38593.836
    下载: 导出CSV

    表  13  单输入通道与多输入通道性能对比

    Table  13  Performance comparison between single channel and multi-channels

    指标$c_{\rm{in}}^1$$c_{\rm{in}}^2$$c_{\rm{in}}^{1,2}$
    RMSE, $\text{var} \in N$0.0840.0460.044
    RMSE, $\text{var} \in F$0.0440.0440.046
    MAE, $\text{var} \in N$0.0720.0380.036
    MAE, $\text{var} \in F$0.0370.0380.039
    $\text{False}_\text{p}$, $\text{var} \in N$22.7035.5274.500
    $\text{False}_\text{n}$, $\text{var} \in N$000
    F1, $\text{var} \in N$87.19597.15897.698
    $\text{False}_\text{p}$, $\text{var} \in F$33.40515.37210.769
    $\text{False}_\text{n}$, $\text{var} \in F$16.76510.0931.056
    F1, $\text{var} \in F$73.99187.18793.836
    下载: 导出CSV

    表  14  自迭代效果对比

    Table  14  Comparison of self iteration effect

    指标$it = 0$$it = 5$$it = 50$
    $\text{False}_\text{p}$, $\text{var} \in N$11.6627.5274.500
    $\text{False}_\text{n}$, $\text{var} \in N$000
    F1, $\text{var} \in N$93.80896.08997.698
    $\text{False}_\text{p}$, $\text{var} \in F$11.74012.28910.769
    $\text{False}_\text{n}$, $\text{var} \in F$1.7320.6761.055
    F1, $\text{var} \in F$93.00093.15793.837
    下载: 导出CSV
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  • 收稿日期:  2022-02-08
  • 录用日期:  2022-09-06
  • 网络出版日期:  2022-10-08

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