Correlation-based spectral clustering for flexible process monitoring

Abstract The individuality of production devices should be taken into account when statistical models are designed for parallelized devices. In the present work, a new clustering method, referred to as NC-spectral clustering, is proposed for discriminating the individuality of production devices. The key idea is to classify samples according to the differences of the correlation among measured variables, since the individuality of production devices is expressed by the correlation. In the proposed NC-spectral clustering, the nearest correlation (NC) method and spectral clustering are integrated. The NC method generates the weighted graph that expresses the correlation-based similarities between samples, and the constructed graph is partitioned by spectral clustering. A new statistical process monitoring method and a new soft-sensor design method are proposed on the basis of NC-spectral clustering. The usefulness of the proposed methods is demonstrated through a numerical example and a case study of parallelized batch processes.

[1]  S. Qin.  Recursive PLS algorithms for adaptive data modeling , 1998 .

[2]  Manabu Kano,et al.  Inferential control system of distillation compositions using dynamic partial least squares regression , 1998 .

[3]  S. Skogestad,et al.  Estimation of distillation compositions from multiple temperature measurements using partial-least-squares regression , 1991 .

[4]  Thomas E. Marlin,et al.  Multivariate statistical monitoring of process operating performance , 1991 .

[5]  M. Birattari,et al.  Lazy learning for local modelling and control design , 1999 .

[6]  Hiromasa Kaneko,et al.  Development of a new soft sensor method using independent component analysis and partial least squares , 2009 .

[7]  Manabu Kano,et al.  Two-stage subspace identification for softsensor design and disturbance estimation , 2009 .

[8]  K. Fujiwara,et al.  Development of correlation-based clustering method and its application to software sensing , 2010 .

[9]  Manfred Morari,et al.  A clustering technique for the identification of piecewise affine systems , 2001, Autom..

[10]  Min-Sen Chiu,et al.  Nonlinear process monitoring using JITL-PCA , 2005 .

[11]  Zhi-huan Song,et al.  Online monitoring of nonlinear multiple mode processes based on adaptive local model approach , 2008 .

[12]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[13]  J. E. Jackson,et al.  Control Procedures for Residuals Associated With Principal Component Analysis , 1979 .

[14]  Morimasa Ogawa,et al.  The state of the art in chemical process control in Japan: Good practice and questionnaire survey , 2010 .

[15]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[16]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[17]  Manabu Kano,et al.  Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry , 2008, Comput. Chem. Eng..

[18]  Koichi Fujiwara,et al.  Development of correlation-based pattern recognition algorithm and adaptive soft-sensor design , 2012 .

[19]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[20]  Manabu Kano,et al.  Optimum quality design system for steel products through locally weighted regression model , 2011 .

[21]  Jay H. Lee,et al.  Subspace identification based inferential control applied to a continuous pulp digester , 1999 .

[22]  Manabu Kano,et al.  Soft‐sensor development using correlation‐based just‐in‐time modeling , 2009 .

[23]  Manabu Kano,et al.  Monitoring independent components for fault detection , 2003 .

[24]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[25]  B. J. Cott,et al.  Temperature control of exothermic batch reactors using generic model control , 1989 .

[26]  Manabu Kano,et al.  Product Quality Estimation and Operating Condition Monitoring for Industrial Ethylene Fractionator , 2004 .

[27]  John F. MacGregor,et al.  Multi-way partial least squares in monitoring batch processes , 1995 .

[28]  M. Fiedler.  Algebraic connectivity of graphs , 1973 .

[29]  J. Macgregor,et al.  Monitoring batch processes using multiway principal component analysis , 1994 .

[30]  Abdul Rahman Mohamed,et al.  Neural networks for the identification and control of blast furnace hot metal quality , 2000 .

[31]  M. Chiu,et al.  A new data-based methodology for nonlinear process modeling , 2004 .

[32]  Theodora Kourti,et al.  Process analysis, monitoring and diagnosis, using multivariate projection methods , 1995 .

[33]  Chris H. Q. Ding,et al.  A min-max cut algorithm for graph partitioning and data clustering , 2001, Proceedings 2001 IEEE International Conference on Data Mining.