基于多样性加权相似度的集成局部加权偏最小二乘软测量建模
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摘要
针对传统即时学习软测量方法仅考虑单一的相似度函数,难以有效处理复杂工业过程中的非线性特性,从而导致模型预测性能受限的问题,提出了一种基于多样性加权相似度(DWS)的集成局部加权偏最小二乘(LWPLS)软测量建模方法.首先采用随机子空间法和高斯混合聚类,构建一组多样性的训练样本子集;然后通过偏最小二乘回归分析确定输入特征权值,从而定义一组多样性加权相似度函数.在线实施阶段,对于任意的查询样本,基于多样性的相似度指标,可建立一组多样性的LWPLS软测量模型,随后引入集成学习策略实现难测变量的融合预测.在数值例子和脱丁烷塔过程中的应用结果表明了该方法的有效性.
Conventional just-in-time(JIT) learning-based soft sensors only employ a single similarity measure and cannot efficiently deal with the nonlinear characteristics of complex industrial processes,resulting in poor prediction performance.To tackle this issue,we propose a soft sensor-modeling method based on ensemble locally weighted partial least squares(ELWPLS) using diverse weighted similarity measures(DWS).First,we create a set of diverse training subsets by repeatedly performing random subspace and Gaussian mixture model clustering.Then,we determine the weights of input variables using PLS regression,thereby allowing us to define a set of diverse weighted similarity measures.During the online implementation phase for an arbitrary query sample,a group of diverse LWPLS models can be built and further combined via ensemble learning to provide the final prediction.The effectiveness and superiority of the proposed DWS-ELWPLS soft sensor method is demonstrated through a numerical example and an industrial debutanizer column process.
Conventional just-in-time(JIT) learning-based soft sensors only employ a single similarity measure and cannot efficiently deal with the nonlinear characteristics of complex industrial processes,resulting in poor prediction performance.To tackle this issue,we propose a soft sensor-modeling method based on ensemble locally weighted partial least squares(ELWPLS) using diverse weighted similarity measures(DWS).First,we create a set of diverse training subsets by repeatedly performing random subspace and Gaussian mixture model clustering.Then,we determine the weights of input variables using PLS regression,thereby allowing us to define a set of diverse weighted similarity measures.During the online implementation phase for an arbitrary query sample,a group of diverse LWPLS models can be built and further combined via ensemble learning to provide the final prediction.The effectiveness and superiority of the proposed DWS-ELWPLS soft sensor method is demonstrated through a numerical example and an industrial debutanizer column process.
引文
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[28]Fujiwara K,Kano M,Hasebe S,et al.Soft-sensor development using correlation-based just-in-time modeling[J].AIChE Journal,2009,55(7):1754-1765.
[29]Fan M,Ge Z Q,Song Z H.Adaptive Gaussian mixture model-based relevant sample selection for JITL soft sensor development[J].Industrial&Engineering Chemistry Research,2014,53(51):19979-19986.
[30]Shigemori H,Kano M,Hasebe S.Optimum quality design system for steel products through locally weighted regression model[J].Journal of Process Control,2011,21(2):293-301.
[31]Kim S,Kano M,Nakagawa H,et al.Estimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection[J].International journal of pharmaceutics,2011,421(2):269-274.
[32]Hazama K,Kano M.Covariance-based locally weighted partial least squares for high-performance adaptive modeling[J].Chemometrics and Intelligent Laboratory Systems,2015,146:55-62.
[33]Yu J.Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach[J].Chemical Engineering Science,2012,82:22-30.
[34]Yu J.Multiway Gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes[J].Industrial&Engineering Chemistry Research,2012,51(40):13227-13237.
[35]Figueiredo M A T,Jain A K.Unsupervised learning of finite mixture models[J].IEEE Transactions on pattern analysis and machine intelligence,2002,24(3):381-396.
[36]Brown G,Wyatt J,Harris R,et al.Diversity creation methods:A survey and categorization[J].Information Fusion,2005,6(1):5-20.
[37]Zhou Z H.Ensemble methods:Foundations and algorithms[M].Florida,USA:Chapman and Hall/CRC,2012.
[38]Li G,Aute V,Azarm S.An accumulative error based adaptive design of experiments for offline metamodeling[J].Structural and Multidisciplinary Optimization,2010,40(1/2/3/4/5/6):137.
[2]曹鹏飞,罗雄麟.化工过程软测量建模方法研究进展[J].化工学报,2013,64(3):788-800.Cao P F,Luo X L.Modeling of soft sensor for chemical process[J].CIESC Journal,2013,64(3):788-800.
[3]Kano M,Nakagawa Y.Data-based process monitoring,process control,and quality improvement:Recent developments and applications in steel industry[J].Computers&Chemical Engineering,2008,32(1):12-24.
[4]Kadlec P,Gabrys B,Strandt S.Data-driven soft sensors in the process industry[J].Computers&Chemical Engineering,2009,33(4):795-814.
[5]Kadlec P,Gabrys B.Local learning-based adaptive soft sensor for catalyst activation prediction[J].AIChE Journal,2011,57(5):1288-1301.
[6]Ge Z Q,Song Z H.Ensemble independent component regression models and soft sensing application[J].Chemometrics and Intelligent Laboratory Systems,2014,130:115-122.
[7]Gonzaga J C B,Meleiro L A C,Kiang C,et al.ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process[J].Computers&Chemical Engineering,2009,33(1):43-49.
[8]Kaneko H,Funastu K.Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants[J].Chemometrics and Intelligent Laboratory Systems,2014,137:57-66.
[9]Jin H P,Chen X G,Yang J W,et al.Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes[J].Chemical Engineering Science,2015,131:282-303.
[10]熊伟丽,张伟,徐保国.一种基于EGMM的高斯过程回归软测量建模[J].信息与控制,2016,45(1):14-19.Xiong W L,Zhang W,Xu B G.A Soft sensor modeling method based on EGMM using Gaussian process regression[J].Information and Control,2016,45(1):14-19.
[11]Xiong W L,Li Y J,Zhao Y J,et al.Adaptive soft sensor based on time difference Gaussian process regression with local time-delay reconstruction[J].Chemical Engineering Research and Design,2017,117:670-680.
[12]Mei C L,Su Y,Liu G H,et al.Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes[J].Chinese Journal of Chemical Engineering,2017,25(1):116-122.
[13]Grbic'R,Sli2kovic'D,Kadlec P.Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models[J].Computers&Chemical Engineering,2013,58:84-97.
[14]Shao W M,Tian X M.Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models[J].Chemical Engineering Research and Design,2015,95:113-132.
[15]Kano M,Fujiwara K.Virtual sensing technology in process industries:Trends and challenges revealed by recent industrial applications[J].Journal of Chemical Engineering of Japan,2013,46(1):1-17.
[16]Jin H P,Chen X G,Yang J W,et al.Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch process[J].Computer&Chemical Engineering,2014,71:77-93.
[17]Aha D W.Editorial[M]//Lazy Learning.Netherlands:Springer,1997:7-10.
[18]Zhou Z H,Yu Y.Ensembling local learners through multimodal perturbation[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,2005,35(4):725-735.
[19]Kim S,Kano M,Hasebe S,et al.Long-term industrial applications of inferential control based on just-in-time soft-sensors:Economical impact and challenges[J].Industrial&Engineering Chemistry Research,2013,52(35):12346-12356.
[20]Yuan X F,Ge Z Q,Huang B,et al.Semisupervised JITL framework for nonlinear industrial soft sensing based on locally semisupervised weighted PCR[J].IEEE Transactions on Industrial Informatics,2017,13(2):532-541.
[21]Yuan X F,Ge Z Q,Huang B,et al.A probabilistic just-in-time learning framework for soft sensor development with missing data[J].IEEETransactions on Control Systems Technology,2017,25(3):1124-1132.
[22]Xiong W L,Zhang W,Xu B G,et al.JITL based MWGPR soft sensor for multi-mode process with dual-updating strategy[J].Computers&Chemical Engineering,2016,90:260-267.
[23]Chen M L,Khare S,Huang B.A unified recursive just-in-time approach with industrial near infrared spectroscopy application[J].Chemometrics and Intelligent Laboratory Systems,2014,135:133-140.
[24]Liu Y,Zhang Z J,Chen J H.Ensemble local kernel learning for online prediction of distributed product outputs in chemical processes[J].Chemical Engineering Science,2015,137:140-151.
[25]Liu Y,Gao Z L.Industrial melt index prediction with the ensemble anti-outlier just-in-time Gaussian process regression modeling method[J].Journal of Applied Polymer Science,2015,132(22):41958(1-10).
[26]Ge Z Q,Song Z H.A comparative study of just-in-time-learning based methods for online soft sensor modeling[J].Chemometrics and Intelligent Laboratory System,2010,1049(2):306-317.
[27]Cheng C,Chiu M-S.A new data-based methodology for nonlinear process modeling[J].Chemical Engineering Science,2004,59(13):2801-2810.
[28]Fujiwara K,Kano M,Hasebe S,et al.Soft-sensor development using correlation-based just-in-time modeling[J].AIChE Journal,2009,55(7):1754-1765.
[29]Fan M,Ge Z Q,Song Z H.Adaptive Gaussian mixture model-based relevant sample selection for JITL soft sensor development[J].Industrial&Engineering Chemistry Research,2014,53(51):19979-19986.
[30]Shigemori H,Kano M,Hasebe S.Optimum quality design system for steel products through locally weighted regression model[J].Journal of Process Control,2011,21(2):293-301.
[31]Kim S,Kano M,Nakagawa H,et al.Estimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection[J].International journal of pharmaceutics,2011,421(2):269-274.
[32]Hazama K,Kano M.Covariance-based locally weighted partial least squares for high-performance adaptive modeling[J].Chemometrics and Intelligent Laboratory Systems,2015,146:55-62.
[33]Yu J.Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach[J].Chemical Engineering Science,2012,82:22-30.
[34]Yu J.Multiway Gaussian mixture model based adaptive kernel partial least squares regression method for soft sensor estimation and reliable quality prediction of nonlinear multiphase batch processes[J].Industrial&Engineering Chemistry Research,2012,51(40):13227-13237.
[35]Figueiredo M A T,Jain A K.Unsupervised learning of finite mixture models[J].IEEE Transactions on pattern analysis and machine intelligence,2002,24(3):381-396.
[36]Brown G,Wyatt J,Harris R,et al.Diversity creation methods:A survey and categorization[J].Information Fusion,2005,6(1):5-20.
[37]Zhou Z H.Ensemble methods:Foundations and algorithms[M].Florida,USA:Chapman and Hall/CRC,2012.
[38]Li G,Aute V,Azarm S.An accumulative error based adaptive design of experiments for offline metamodeling[J].Structural and Multidisciplinary Optimization,2010,40(1/2/3/4/5/6):137.