A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

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• 作者：Shi-jin Ren ; Yin Liang ; Xiang-jun Zhao…
• 关键词：Multimode process monitoring ; Local discriminant regularized soft k ; means clustering ; Kernel support vector data description ; Bayesian inference ; Tennessee Eastman process ; A ; TP277
• 刊名：Frontiers of Information Technology & Electronic Engineering
• 出版年：2015
• 出版时间：August 2015
• 年：2015
• 卷：16
• 期：8
• 页码：617-633
• 全文大小：780 KB
• 参考文献：Cai, L.F., Tian, X.M., Zhang, N., 2014. A kernel time structure independent component analysis method for nonlinear process monitoring. Chin. J. Chem. Eng., 22(11-12): 1243鈥?253. [doi:10.1016/j.cjche.2014.09.021]CrossRef
  Chiang, L.H., Russell, E.L., Braatz, R.D., 2000. Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chemometr. Intell. Lab. Syst., 50(2):243鈥?52. [doi:10.1016/S0169-7439(99)00061-1]CrossRef
  Deng, X.G., Tian, X.M., 2013. Sparse kernel locality preserving projection and its application in nonlinear process fault detection. Chin. J. Chem. Eng., 21(2):163鈥?70. [doi:10.1016/S1004-9541(13)60454-1]MathSciNet CrossRef
  Deng, X.G., Tian, X.M., Chen, S., 2013) Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis. Chemometr. Intell. Lab. Syst., 127: 195鈥?09. [doi:10.1016/j.chemolab.2013.07.001]CrossRef
  Dong, W.W., Yao, Y., Gao, F.R., 2012. Phase analysis and identification method for multiphase batch processes with partitioning multi-way principal component analysis (MPCA) model. Chin. J. Chem. Eng., 20(6):1121鈥?127. [doi:10.1016/S1004-9541(12)60596-5]CrossRef
  Downs, J.J., Vogel, E.F., 1993. A plant-wide industrial process control problem. Comput. Chem. Eng., 17(3):245鈥?55. [doi:10.1016/0098-1354(93)80018-I]CrossRef
  Feital, T., Kruger, U., Dutra, J., et al., 2013. Modeling and performance monitoring of multivariate multimodal processes. AIChE J., 59(5):1557鈥?569. [doi:10.1002/aic.13953]CrossRef
  Ge, Z.Q., Song, Z.H., 2010. Maximum-likelihood mixture factor analysis model and its application for process monitoring. Chemometr. Intell. Lab. Syst., 102(1):53鈥?1. [doi:10.1016/j.chemolab.2010.04.002]CrossRef
  Ge, Z.Q., Song, Z.H., 2012. A distribution-free method for process monitoring. Exp. Syst. Appl., 38(8):9812鈥?829. [doi:10.1016/j.eswa.2011.02.048]
  Ge, Z.Q., Zhang, M.G., Song, Z.H., 2010. Nonlinear process monitoring based on linear subspace and Bayesian inference. J. Process Contr., 20(5):676鈥?88. [doi:10.1016/j.jprocont.2010.03.003]CrossRef
  Ge, Z.Q., Song, Z.H., Gao, F.R., 2013. Review of recent research on data-based process monitoring. Ind. Eng. Chem. Res., 52(10):3543鈥?562. [doi:10.1021/ie302069q]CrossRef MATH
  Ghosh, K., Ramteke, M., Srinivasan, R., 2014) Optimal variable selection for effective statistical process monitoring. Comput. Chem. Eng., 60: 260鈥?76. [doi:10.1016/j.compchemeng.2013.09.014]CrossRef
  He, X.F., Cai, D., Shao, Y.L., et al., 2011. Laplacian regularized Gaussian mixture model for data clustering. IEEE Trans. Knowl. Data Eng., 23(9):1406鈥?418. [doi:10.1109/TKDE.2010.259]CrossRef
  Howland, P., Wang, J., Park, H., 2006. Solving the small sample size problem in face recognition using generalized discriminant analysis. Patt. Recog., 39(2):277鈥?87. [doi:10.1016/j.patcog.2005.06.013]CrossRef
  Jing, L.P., Ng, M.K., Huang, J.Z., 2007. An entropy weighting k-means algorithm for subspace clustering of highdimensional sparse data. IEEE Trans. Knowl. Data Eng., 19(8):1026鈥?041. [doi:10.1109/TKDE.2007.1048]CrossRef
  Kano, M., Nagao, K., Hasebe, S., et al., 2002. Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem. Comput. Chem. Eng., 26(2):161鈥?74. [doi:10.1016/S0098-1354(01)00738-4]CrossRef
  Kano, M., Fujioka, T., Tonomura, O., et al., 2007. Data-based and model-based blockage diagnosis for stacked microchemical processes. Chem. Eng. Sci., 62(4):1073鈥?080. [doi:10.1016/j.ces.2006.11.011]CrossRef
  Lee, D., Lee, J., 2007. Domain described support vector classifier for multi-classification problems. Patt. Recog., 40(1):41鈥?1. [doi:10.1016/j.patcog.2006.06.008]CrossRef MATH
  Lee, J., Kang, B., Kang, S., 2011. Integrating independent component analysis and local outlier factor for plant-wide process monitoring. J. Process Contr., 21(7):1011鈥?021. [doi:10.1016/j.jprocont.2011.06.004]CrossRef
  Liu, J.L., Cai, D., He, X.F., 2010. Gaussian mixture model with local consistency. Proc. 24th AAAI Conf. on Artificial Intelligence, p.512鈥?17.
  Miao, A.M., Ge, Z.Q., Song, Z.H., et al., 2015) Nonlocal structure constrained neighborhood preserving embedding model and its application for fault detection. Chemometr. Intell. Lab. Syst., 142: 184鈥?96. [doi:10.1016/j.chemolab.2015.01.010]CrossRef
  Miyamoto, S., Mukaidono, M., 1997. Fuzzy C-means as a regularization and maximum entropy approach. Proc. IFSA, p.86鈥?2.
  Molina, G.D., Zumoffen, D.A.R., Basualdo, M.S., 2011. Plant-wide control strategy applied to the Tennessee Eastman process at two operating points. Comput. Chem. Eng., 35(10):2081鈥?097. [doi:10.1016/j.compchemeng.2010.11.006]CrossRef
  Ng, Y.S., Srinivasan, R., 2009. An adjoined multi-model approach for monitoring batch and transient operations. Comput. Chem. Eng., 33(4):887鈥?02. [doi:10.1016/j.compchemeng.2008.11.014]CrossRef
  Perez, C.F.A., 2011. Fault Diagnosis with Reconstruction- Based Contributions for Statistical Process Monitoring. PhD Thesis, University of Southern California USA.
  Serradilla, J., Shi, J.Q., Morris, A.J., 2011. Fault detection based on Gaussian process latent variable models. Chemometr. Intell. Lab. Syst., 109(1):9鈥?1. [doi:10.1016/j.chemolab.2011.07.003]CrossRef
  Shen, J.F., Bu, J.J., Ju, B., et al., 2012) Refining Gaussian mixture model based on enhanced manifold learning. Neurocomputing, 87: 19鈥?5. [doi:10.1016/j.neucom.2012.01.029]CrossRef
  Song, B., Ma, Y.X., Shi, H.B., 2014) Multimode process monitoring using improved dynamic neighborhood preserving embedding. Chemometr. Intell. Lab. Syst., 135: 17鈥?0. [doi:10.1016/j.chemolab.2014.03.013]CrossRef
  Tan, S.C., Lim, C.P., Rao, M.V.C., 2007. A hybrid neural network model for rule generation and its application to process fault detection and diagnosis. Eng. Appl. Artif. Intell., 20(2):203鈥?13. [doi:10.1016/j.engappai.2006.06.007]CrossRef
  Teppola, P., Mujunen, S.P., Minkkinen, P., 1999. Adaptive fuzzy C-means clustering in process monitoring. Chemometr. Intell. Lab. Syst., 45(1-2):23鈥?8. [doi:10.1016/S0169-7439(98)00087-2]CrossRef
  Tong, C.D., Palazoglu, A., Yan, X.F., 2013. An adaptive multimode process monitoring strategy based on mode clustering and mode unfolding. J. Process Contr., 23(10):1497鈥?507. [doi:10.1016/j.jprocont.2013.09.017]CrossRef
  Venkatasubramanian, V., Rengaswamy, R., Yin, K., et al., 2003. A review of process fault detection and diagnosis: Part I: quantitative model-based methods. Comput. Chem. Eng., 27(3):293鈥?11. [doi:10.1016/S0098-1354(02)00160-6]CrossRef
  Xie, L., Liu, X.Q., Zhang, J.M., et al., 2009. Non-Gaussian process monitoring based on NGPP-SVDD. Acta Autom. Sin., 35(1):107鈥?12 (in Chinese).MathSciNet CrossRef
  Xie, X., Shi, H.B., 2012. Multimode process monitoring based on fuzzy C-means in locality preserving projection subspace. Chin. J. Chem. Eng., 20(6):1174鈥?179. [doi:10.1016/S1004-9541(12)60604-1]CrossRef
  Xu, X., Xie, L., Wang, S., 2011. Multi-mode process monitoring method based on PCA mixture model. CIESC J., 62(3):743鈥?52 (in Chinese).
  Yang, Y.H., Li, X., Liu, X.Z., et al., 2015) Wavelet kernel entropy component analysis with application to industrial process monitoring. Neurocomputing, 147: 395鈥?02. [doi:10.1016/j.neucom.2014.06.045]CrossRef
  Yin, X.S., Chen, S.C., Hu, E.L., 2013) Regularized soft K-means for discriminant analysis. Neurocomputing, 103: 29鈥?2. [doi:10.1016/j.neucom.2012.08.021]CrossRef
  Yu, J., 2012. A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes. Chem. Eng. Sci., 68(1):506鈥?19. [doi:10.1016/j.ces.2011.10.011]CrossRef
  Zang, X., Vista Iv, F.P., Chong, K.T., 2014. Fast global kernel fuzzy c-means clustering algorithm for consonant/vowel segmentation of speech signal. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 15(7):551鈥?63. [doi:10.1631/jzus.C1300320]CrossRef
  Zhang, M., Ge, Z.Q., Song, Z.H., et al., 2011. Global-local structure analysis model and its application for fault detection and identification. Ind. Eng. Chem. Res., 50(11): 6837鈥?848. [doi:10.1021/ie102564d]CrossRef
  Zhang, S.J., Wang, Z.L., Qian, F., 2010. FS-SVDD based on LTSA and its application to chemical process monitoring. CIESC J., 61(8):1894鈥?900 (in Chinese).
  Zhang, Y.W., 2009. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM. Chem. Eng. Sci., 64(5):801鈥?11. [doi:10.1016/j.ces.2008.10.012]CrossRef
  Zhang, Y.W., Li, S., 2014. Modeling and monitoring of nonlinear multi-mode processes. Contr. Eng. Pract., 22: 194鈥?04. [doi:10.1016/j.conengprac.2013.04.007]CrossRef
  Zhang, Y.W., An, J.Y., Li, Z.M., et al., 2013) Modeling and monitoring for handling nonlinear dynamic processes. Inform. Sci., 235: 97鈥?05. [doi:10.1016/j.ins.2012.04.023]MathSciNet CrossRef MATH
  Zhu, Z.B., Wang, P.L., Song, Z.H., 2010. PCA-SVDD based fault detection and self-learning identification. J. Zhejiang Univ. (Eng. Sci.), 44(4):652鈥?58 (in Chinese).
  Zhu, Z.B., Song, Z.H., Palazoglu, A., 2012. Process pattern construction and multi-mode monitoring. J. Process Contr., 22(1):247鈥?62. [doi:10.1016/j.jprocont.2011.08. 002]CrossRef
  
• 作者单位：Shi-jin Ren (1) (2)
  Yin Liang (2)
  Xiang-jun Zhao (2)
  Mao-yun Yang (2)
  
  1. National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, China
  2. School of Computer Science & Technology, Jiangsu Normal University, Xuzhou, 221116, China
  
• 刊物类别：Computer Science, general; Electrical Engineering; Computer Hardware; Computer Systems Organization
• 刊物主题：Computer Science, general; Electrical Engineering; Computer Hardware; Computer Systems Organization and Communication Networks; Electronics and Microelectronics, Instrumentation; Communications Engine
• 出版者：Zhejiang University Press
• ISSN：2095-9230

文摘

A local discriminant regularized soft k-means (LDRSKM) method with Bayesian inference is proposed for multimode process monitoring. LDRSKM extends the regularized soft k-means algorithm by exploiting the local and non-local geometric information of the data and generalized linear discriminant analysis to provide a better and more meaningful data partition. LDRSKM can perform clustering and subspace selection simultaneously, enhancing the separability of data residing in different clusters. With the data partition obtained, kernel support vector data description (KSVDD) is used to establish the monitoring statistics and control limits. Two Bayesian inference based global fault detection indicators are then developed using the local monitoring results associated with principal and residual subspaces. Based on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode correlations, and local geometric information can be exploited to enhance monitoring performances for nonlinear and non-Gaussian processes. The effectiveness and efficiency of the proposed method are evaluated using the Tennessee Eastman benchmark process. Keywords Multimode process monitoring Local discriminant regularized soft k-means clustering Kernel support vector data description Bayesian inference Tennessee Eastman process