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多模态过程的全自动离线模态识别方法

张淑美 王福利 谭帅 王姝

张淑美, 王福利, 谭帅, 王姝. 多模态过程的全自动离线模态识别方法. 自动化学报, 2016, 42(1): 60-80. doi: 10.16383/j.aas.2016.c150048
引用本文: 张淑美, 王福利, 谭帅, 王姝. 多模态过程的全自动离线模态识别方法. 自动化学报, 2016, 42(1): 60-80. doi: 10.16383/j.aas.2016.c150048
ZHANG Shu-Mei, WANG Fu-Li, TAN Shuai, WANG Shu. A Fully Automatic Offline Mode Identification Method for Multi-mode Processes. ACTA AUTOMATICA SINICA, 2016, 42(1): 60-80. doi: 10.16383/j.aas.2016.c150048
Citation: ZHANG Shu-Mei, WANG Fu-Li, TAN Shuai, WANG Shu. A Fully Automatic Offline Mode Identification Method for Multi-mode Processes. ACTA AUTOMATICA SINICA, 2016, 42(1): 60-80. doi: 10.16383/j.aas.2016.c150048

多模态过程的全自动离线模态识别方法

doi: 10.16383/j.aas.2016.c150048
基金项目: 

流程工业综合自动化国家重点实验室基础科研业务费 2013ZCX02-04

华东理工大学探索研究专项基金 22A201514050

中央高校基本科研专项资金 N140404020

国家自然科学基金 61533007, 61374146, 61403072

详细信息
    作者简介:

    王福利 东北大学教授.主要研究方向为复杂工业过程建模、控制与优化,工业过程监测、质量预报与故障诊断.E-mail:wangfuli@ise.neu.edu.cn

    谭帅 华东理工大学讲师.主要研究方向为复杂工业过程建模,过程监测与故障诊断.E-mail:tanshuai@ecust.edu.cn

    王姝 东北大学副教授.主要研究方向为复杂工业过程建模,过程监测与故障诊断.E-mail:wangshu@ise.neu.edu.cn

    通讯作者:

    张淑美 东北大学博士研究生.主要研究方向为复杂工业过程监测与故障诊断.本文通信作者.E-mail:aries816@163.com

A Fully Automatic Offline Mode Identification Method for Multi-mode Processes

Funds: 

State Key Laboratory of Synthetical Automation for Process Industries Fundamental Research Funds 2013ZCX02-04

and the Fundamental Research Funds for East China University of Science and Technology 22A201514050

the Fundamental Research Funds for the Central Universities N140404020

Supported by National Natural Science Foundation of China 61533007, 61374146, 61403072

More Information
    Author Bio:

    Professor at Northeastern University. His research interest covers complex industrial process modeling, control and optimization, industrial process monitoring, quality prediction, and fault diagnosis

    Lecturer at East China University of Science and Technology. Her research interest covers complex industrial process modeling, process monitoring, and fault diagnosis

    Associate professor at Northeastern University. Her research interest covers complex industrial process modeling, process monitoring, and fault diagnosis

    Corresponding author: ZHANG Shu-Mei Ph.D. candidate at Northeastern University. Her research interest covers complex industrial process monitoring and fault diagnosis. Corresponding author of this paper
  • 摘要: 多模态是复杂工业生产过程的普遍特性.不同模态具有不同的过程特性,需要建立不同的模型,因此离线建模数据的模态划分与识别是整个多模态过程建模的关键问题之一.目前,常用的聚类算法需要对其结果进行人工分析和后续处理,无法真正实现多模态过程的全自动模态识别.因此,本文提出一种全自动的多模态过程离线模态识别方法.首先通过宽度为H的大切割窗口对数据进行切割,利用改进的K-means聚类算法对窗口单元进行聚类;根据聚类结果,对稳定模态淹没现象进行处理,得到模态的初步划分结果;最终,利用小滑动窗口L,对稳定模态及过渡模态交接区域进行细划分,准确定位稳定模态与过渡模态的分割点.算法实现了多模态过程的全自动离线识别,并给出合理有效的识别结果.仿真分析表明此方法能够实现模态的自动识别,且识别结果准确.
  • 图  1  多模态过程的模态初步识别过程

    Fig.  1  Preliminary mode identification for multimode processes

    图  2  多模态准确识别过程(以 $AC$ 过渡模态起始时刻为例)

    Fig.  2  Exact mode identification for multimode processes (i.e.beginning of the transitional mode $AC$ )

    图  3  TE过程15个变量变化曲线 ( $X$ 轴表示采样点)

    Fig.  3  15 variables of the TE process ( $X$ axis indicates sample points.)

    图  4  K-means算法聚类结果 ( $H=1$ )

    Fig.  4  Clustering result of K-means algorithm ( $H=1$ )

    图  5  前后两次迭代聚类中心的距离( $H=1$ )

    Fig.  5  Distance between clustering centers in two iterations ( $H=1$ )

    图  6  聚类中心个数( $H=1$ )

    Fig.  6  Number of clustering centers ( $H=1$ )

    图  7  K-means算法聚类结果 ( $H=50$ )

    Fig.  7  Clustering result of K-means algorithm ( $H=50$ )

    图  8  前后两次迭代聚类中心的距离( $H=50$ )

    Fig.  8  Distance between clustering centers in two iterations ( $H=50$ )

    图  9  聚类中心个数 ( $H=50$ )

    Fig.  9  Number of clustering centers ( $H=50$ )

    图  10  K-means算法聚类结果 ( $H=500$ )

    Fig.  10  Clustering result of K-means algorithm ( $H=500$ )

    图  11  前后两次迭代聚类中心的距离 ( $H=500$ )

    Fig.  11  Distance between clustering centers in two iterations( $H=500$ )

    图  12  聚类中心个数 ( $H=500$ )

    Fig.  12  Number of clustering centers ( $H=500$ )

    图  13  初始聚类中心到第一个聚类中心的距离( $H=1$ )

    Fig.  13  Distance between original clustering center and the first clustering center ( $H=1$ )

    图  14  初始聚类中心到第一个聚类中心的距离( $H=50$ )

    Fig.  14  Distance between original clustering center and the first clustering center ( $H=50$ )

    图  15  初始聚类中心到第一个聚类中心的距离( $H=500$ )

    Fig.  15  Distance between original clustering center and the first clustering center ( $H=500$ )

    图  16  模态初步识别结果 ( $H=50$ )

    Fig.  16  Preliminary mode identification result ( $H=50$ )

    图  17  $AB$ 过渡过程起始位置识别 ( $L=1$ )

    Fig.  17  Identification at the beginning of $AB$ mode( $L=1$ )

    图  18  $AB$ 过渡过程起始位置识别 ( $L=5$ )

    Fig.  18  Identification at the beginning of $AB$ mode( $L=5$ )

    图  19  $AB$ 过渡过程终止位置识别 ( $L=1$ )

    Fig.  19  Identification at the end of $AB$ mode ( $L=1$ )

    图  20  $AB$ 过渡过程终止位置识别 ( $L=5$ )

    Fig.  20  Identification at the end of $AB$ mode ( $L=5$ )

    图  21  $BC$ 过渡过程起始位置识别 ( $L=1$ )

    Fig.  21  Identification at the beginning of $BC$ mode ( $L=1$ )

    图  22  $BC$ 过渡过程起始位置识别 ( $L=5$ )

    Fig.  22  Identification at the beginning of $BC$ mode ( $L=5$ )

    图  23  $BC$ 过渡过程终止位置识别 ( $h=1$ )

    Fig.  23  Identification at the end of $BC$ mode ( $h=1$ )

    图  24  $BC$ 过渡过程终止位置识别 ( $h=6$ )

    Fig.  24  Identification at the end of $BC$ mode ( $h=6$ )

    图  25  $CA$ 过渡过程起始位置识别( $L=5,h=1$ )

    Fig.  25  Identification at the beginning of $CA$ mode ( $L=5,h=1$ )

    表  1  TE过程操作模态列表

    Table  1  Operating modes of Tennessee Eastman process

    模态类型 反应器压力设定值(kPa) 反应器液位(%)
    稳定模态 $A_0$ 2800 65
    稳定模态 $B_0$ 2600 65
    稳定模态 $C_0$ 2400 65
    扰动1 2800 75
    下载: 导出CSV

    表  2  TE过程变量表

    Table  2  Variables of Tennessee Eastman process

    序号 变量名称
    1 $A$ 进料量(流1)
    2 $D$ 进料量(流2)
    3 $E$ 进料量(流3)
    4 $A,C$ 混合物料流量
    5 再循环流量(流8)
    6 反应器进料速度(流6)
    7 反应器温度
    8 排放速度(流9)
    9 产品分离器温度
    10 产品分离器压力
    11 产品分离器塔底流量(流10)
    12 汽提塔压力
    13 汽提塔温度
    14 反应器冷却水出口温度
    15 分离器冷却水出口温度
    下载: 导出CSV

    表  3  TE过程实验设计

    Table  3  Experimental design of TE process

    模态 聚类单元
    $A_0-1$ 1 $\sim$ 3000
    $A_0B_0$ 3001 $\sim$ 3520
    $B_0$ 3521 $\sim$ 7000
    $B_0C_0$ 7001 $\sim$ 8028
    $C_0$ 8029 $\sim$ 11000
    $C_0A_0$ 11001 $\sim$ 12235
    $A_0-2$ 12236 $\sim$ 14000
    扰动 14001 $\sim$ 14720
    $A_0-3$ 14721 $\sim$ 18000
    下载: 导出CSV

    表  4  各时段运行时间( $H=50$ )

    Table  4  Runtime of each period ( $H=50$ )

    时段 窗口个数 运行时间 (h) 所属类别
    1 60 30 1
    2 3 1.5 3
    3 7 3.5 6
    4 70 35 4
    5 3 1.5 2
    6 1 0.5 5
    7 7 3.5 6
    8 9 4.5 8
    9 11 5.5 5
    10 19 9.5 7
    11 10 5 5
    12 6 3 7
    13 9 4.5 5
    14 11 5.5 7
    15 7 3.5 3
    16 10 5 4
    17 37 18.5 1
    18 3 1.5 3
    19 12 6 2
    20 65 32.5 1
    下载: 导出CSV

    表  5  聚类结果比较(不同 $H$ 值)

    Table  5  Comparison of clustering results (different $H$ )

    $H$ 值 聚类单元 初始类别数量 最终类别数量 迭代次数 运行时间(s)
    1 18000 6000 20 52 519.2
    50 360 120 8 4 0.906
    500 36 12 3 3 0.745
    下载: 导出CSV

    表  6  识别结果比较 (不同 $L$ 值)

    Table  6  Comparison of clustering results (different $L$ )

    模态 小窗口 $L$ 相似度阈值 识别位置(采样点) 识别误差(采样点)
    $AB$ 起始时刻 $L=1$ $\alpha=0.95$ 2983 18
    $\alpha=0.85$ 3007 6
    弹性阈值 $\alpha$ 3006 5
    $L=5$ $\alpha=0.95$ 30010
    $\alpha=0.85$ 30065
    弹性阈值 $\alpha$ 30010
    $AB$ 终止时刻 $L=1$ $\alpha=0.95$ 355030
    $\alpha=0.85$ 345862
    弹性阈值 $\alpha$ 348238
    $L=5$ $\alpha=0.95$ 355030
    $\alpha=0.85$ 345131
    弹性阈值 $\alpha$ 35182
    下载: 导出CSV

    表  7  识别结果比较(不同 $h$ 值)

    Table  7  Comparison of clustering results (different $h$ )

    模态 小窗口 $L$ 相似度阈值 识别位置(采样点) 识别误差(采样点)
    $BC$ 起始时刻 $h=1$ $\alpha=0.95$ 70010
    $\alpha=0.85$ 70065
    弹性阈值 $\alpha$ 70010
    $h=5$ $\alpha=0.95$ 70098
    $\alpha=0.85$ 701514
    弹性阈值 $\alpha$ 70098
    $BC$ 终止时刻 $h=1$ $\alpha=0.95$ 805022
    $\alpha=0.85$ 795177
    弹性阈值 $\alpha$ 80271
    $h=5$ $\alpha=0.95$ 804517
    $\alpha=0.85$ 795177
    弹性阈值 $\alpha$ 80335
    下载: 导出CSV

    表  8  TE过程识别结果

    Table  8  Identification result of TE process

    模态 聚类单元 模态 聚类单元 模态 聚类单元
    $A-1$ 1~3 000 $BC$ 7 001~8 027 $A-2$ 12 238 ~14 000
    $AB$ 3 001~3 518 $C$ 8 028~11 250 扰动 14 001 ~14 725
    $B$ 3 519~7 000 $CA$ 11 251~12 237 $A-3$ 14 726 ~18 000
    下载: 导出CSV
  • [1] 谭帅. 多模态过程统计建模及在线监测方法研究[博士学位论文], 东北大学, 中国, 2012.

    Tan Shuai. Statistical Modeling and Online Monitoring for Multiple Mode Processes[Ph.D. dissertation], Northeastern University, China, 2012.
    [2] Yu J, Qin S J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE Journal, 2008, 54(7):1811-1829 doi: 10.1002/(ISSN)1547-5905
    [3] 王静, 胡益, 侍洪波. 基于GMM的间歇过程故障检测. 自动化学报, 2015, 41(5):899-905 http://www.aas.net.cn/CN/abstract/abstract18664.shtml

    Wang Jing, Hu Yi, Shi Hong-Bo. Fault detection for batch processes based on Gaussian mixture model. Acta Automatica Sinica, 2015, 41(5):899-905 http://www.aas.net.cn/CN/abstract/abstract18664.shtml
    [4] Xie X, Shi H B. Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models. Industrial and Engineering Chemistry Research, 2012, 51(15):5497-5505 doi: 10.1021/ie202720y
    [5] Ge Z Q, Gao F R, Song Z H. Mixture probabilistic PCR model for soft sensing of multimode processes. Chemometrics and Intelligent Laboratory Systems, 2011, 105(1):91-105 doi: 10.1016/j.chemolab.2010.11.004
    [6] Ge Z Q. Mixture Bayesian regularization of PCR model and soft sensing application. IEEE Transactions on Industrial Electronics, 2015, 62(7):4336-4343 doi: 10.1109/TIE.2014.2385042
    [7] Ng Y S, Srinivasan R. An adjoined multi-model approach for monitoring batch and transient operations. Computers and Chemical Engineering, 2009, 33(4):887-902 doi: 10.1016/j.compchemeng.2008.11.014
    [8] Lu N Y, Gao F R, Wang F L. Sub-PCA modeling and on-line monitoring strategy for batch processes. AIChE Journal, 2004, 50(1):255-259 doi: 10.1002/(ISSN)1547-5905
    [9] Zhao C H, Wang F L, Lu N Y, Jia M X. Stage-based soft-transition multiple PCA modeling and on-line monitoring strategy for batch processes. Journal of Process Control, 2007, 17(9):728-741 doi: 10.1016/j.jprocont.2007.02.005
    [10] Zhao C H, Zhang W D. Reconstruction based fault diagnosis using concurrent phase partition and analysis of relative changes for multiphase batch processes with limited fault batches. Chemometrics and Intelligent Laboratory Systems, 2014, 130:135-150 doi: 10.1016/j.chemolab.2013.10.014
    [11] Tang X C, Li Y, Xie Z. Phase division and process monitoring for multiphase batch processes with transitions. Chemometrics and Intelligent Laboratory Systems, 2015, 145:72-83 doi: 10.1016/j.chemolab.2015.04.007
    [12] Wang F L, Tan S, Peng J, Chang Y Q. Process monitoring based on mode identification for multi-mode process with transitions. Chemometrics and Intelligent Laboratory Systems, 2012, 110(1):144-155 doi: 10.1016/j.chemolab.2011.10.013
    [13] Tan S, Wang F L, Peng J, Chang Y Q, Wang S. Multimode process monitoring based on mode identification. Industrial and Engineering Chemistry Research, 2012, 51(1):374-388 doi: 10.1021/ie102048f
    [14] Zhang Y W, Zhang H L. Fault detection for time-varying processes. IEEE Transactions on Control Systems Technology, 2014, 22(4):1527-1535 doi: 10.1109/TCST.2013.2273498
    [15] Zhang Y W, Li S. Modeling and monitoring between-mode transition of multimodes processes. IEEE Transactions on Industrial Informatics, 2013, 9(4):2248-2255 doi: 10.1109/TII.2012.2220977
    [16] Alguwaizani A. Degeneracy on K-means clustering. Electronic Notes in Discrete Mathematics, 2012, 39:13-20 doi: 10.1016/j.endm.2012.10.003
    [17] 潘天红, 薛振框, 李少远. 基于减法聚类的多模型在线辨识算法. 自动化学报, 2009, 35(2):220-224 doi: 10.3724/SP.J.1004.2009.00220

    Pan Tian-Hong, Xue Zhen-Kuang, Li Shao-Yuan. An online multi-model identification algorithm based on subtractive clustering. Acta Automatica Sinica, 2009, 35(2):220-224 doi: 10.3724/SP.J.1004.2009.00220
    [18] Chiu S L. Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems:Applications in Engineering and Technology, 1994, 2(3):267-278 http://www.oalib.com/references/16296749
    [19] Jain A K, Murty M N, Flynn P J. Data clustering:a review. ACM Computing Surveys, 1999, 31(3):264-323 doi: 10.1145/331499.331504
    [20] 王惠文. 偏最小二乘回归方法及其应用. 北京:国防工业出版社, 1999.

    Wang Hui-Wen. Partial Least-Squares Regression-Method and Applications. Beijing, China:National Defence Industry Press, 1999.
    [21] 钱鹏江, 王士同, 邓赵红. 快速核密度估计定理和大规模图论松弛聚类方法. 自动化学报, 2011, 37(12):1422-1434

    Qian Peng-Jiang, Wang Shi-Tong, Deng Zhao-Hong. Fast kernel density estimate theorem and scaling up graph-based relaxed clustering method. Acta Automatica Sinica, 2011, 37(12):1422-1434
    [22] 蒋浩天, 拉塞尔 E L, 布拉茨 R D. 工业系统的故障检测与诊断. 北京:机械工业出版社, 2003.

    Chiang L H, Russell E L, Braatz R D. Fault Detection and Diagnosis in Industrial Systems. Beijing, China:China Machine Press, 2003.
    [23] Larsson T, Hestetun K, Hovland E, Skogestad S. Self-optimizing control of a large-scale plant:the Tennessee Eastman process. Industrial and Engineering Chemistry Research, 2001, 40(22):4889-4901 doi: 10.1021/ie000586y
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  • 收稿日期:  2015-03-04
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