Statistical process and controller performance monitoring. A tutorial on current methods and future directions

Statistical process control procedures for a single variable such as Shewhart, CUSUM and EWMA charts are summarized. Multivariable statistical process monitoring (SPM) techniques for both continuous and batch processes are presented. SPM methods based on principal components analysis, canonical variate state space models, partial least squares, multiway and multiblock techniques are discussed. Methods for monitoring single loop and multivariable controller performance are also presented.

[1]  John F. MacGregor,et al.  Multivariate SPC charts for monitoring batch processes , 1995 .

[2]  Karlene A. Kosanovich,et al.  Improved Process Understanding Using Multiway Principal Component Analysis , 1996 .

[3]  A. Negiz,et al.  Statistical monitoring of multivariable dynamic processes with state-space models , 1997 .

[4]  Barry M. Wise,et al.  The process chemometrics approach to process monitoring and fault detection , 1995 .

[5]  Ali Cinar,et al.  Controller performance assessment by frequency domain techniques , 1997 .

[6]  L. E. Wangen,et al.  A multiblock partial least squares algorithm for investigating complex chemical systems , 1989 .

[7]  A. Çinar,et al.  PLS, balanced, and canonical variate realization techniques for identifying VARMA models in state space , 1997 .

[8]  F. Alt,et al.  Choosing principal components for multivariate statistical process control , 1996 .

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

[10]  I. Jolliffe Principal Component Analysis and Factor Analysis , 1986 .

[11]  W. Larimore System Identification, Reduced-Order Filtering and Modeling via Canonical Variate Analysis , 1983, 1983 American Control Conference.

[12]  T. W. Anderson An Introduction to Multivariate Statistical Analysis , 1959 .

[13]  Alf Isaksson,et al.  A modified index for control performance assessment , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[14]  Ali Cinar,et al.  Intelligent process monitoring by interfacing knowledge-based systems and multivariate statistical monitoring , 2000 .

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

[16]  R. Russell Rhinehart A watchdog for controller performance monitoring , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[17]  Nf F. Thornhill,et al.  Performance assessment and diagnosis of refinery control loops , 1998 .

[18]  A. J. Morris,et al.  Comparison of Methods for Handling Unequal Length Batches , 1998 .

[19]  Aaron E. Rosenberg,et al.  Performance tradeoffs in dynamic time warping algorithms for isolated word recognition , 1980 .

[20]  Aaron E. Rosenberg,et al.  Considerations in dynamic time warping algorithms for discrete word recognition , 1978 .

[21]  S. Wold,et al.  The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses , 1984 .

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

[23]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[24]  Ferhan Kayihan,et al.  Full CD Profile Control of Sheet Forming Processes using Adaptive PCA and Reduced Order MPC Design , 1997 .

[25]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[26]  B. Everitt,et al.  Three-Mode Principal Component Analysis. , 1986 .

[27]  T. J. Harris,et al.  Performance assessment of multivariable feedback controllers , 1996, Autom..

[28]  Masanao Aoki,et al.  State Space Modeling of Time Series , 1987 .

[29]  Theodora Kourti,et al.  Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS , 1995 .

[30]  J. Macgregor,et al.  Monitoring and Diagnosing Process Control Performance: The Single-Loop Case , 1991, 1991 American Control Conference.

[31]  Age K. Smilde,et al.  Three-way analyses problems and prospects , 1992 .

[32]  A. Höskuldsson PLS regression methods , 1988 .

[33]  G.A. Dumont,et al.  Control loop performance monitoring , 1996, IEEE Trans. Control. Syst. Technol..

[34]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[35]  Manfred Morari,et al.  Performance monitoring of control systems using likelihood methods , 1996, Autom..

[36]  S. Wold Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models , 1978 .

[37]  Karl Pearson F.R.S. LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[38]  T. McAvoy,et al.  Batch tracking via nonlinear principal component analysis , 1996 .

[39]  Theodora Kourti,et al.  Multivariate SPC Methods for Process and Product Monitoring , 1996 .

[40]  W. T. Tucker,et al.  Algorithmic Statistical Process Control: An Elaboration , 1993 .

[41]  P. A. Taylor,et al.  Synchronization of batch trajectories using dynamic time warping , 1998 .

[42]  S. Wold,et al.  Multi‐way principal components‐and PLS‐analysis , 1987 .

[43]  A. Cinar,et al.  Intelligent process control with supervisory knowledge-based systems , 1994, IEEE Control Systems.

[44]  B. Kowalski,et al.  Theory of medium‐rank second‐order calibration with restricted‐Tucker models , 1994 .

[45]  T. Harris Assessment of Control Loop Performance , 1989 .

[46]  Jay H. Lee,et al.  Diagnostic Tools for Multivariable Model-Based Control Systems , 1997 .

[47]  John F. MacGregor,et al.  Process monitoring and diagnosis by multiblock PLS methods , 1994 .

[48]  T. Harris,et al.  Performance assessment measures for univariate feedback control , 1992 .

[49]  Ali Cinar,et al.  Intelligent Process Monitoring by Interfacing Knowledge-Based Systems and Multivariate SPC Tools , 1997 .

[50]  Paul Geladi,et al.  Analysis of multi-way (multi-mode) data , 1989 .

[51]  B. Bakshi Multiscale PCA with application to multivariate statistical process monitoring , 1998 .

[52]  Pieter M. Kroonenberg,et al.  Three-mode principal component analysis : theory and applications , 1983 .

[53]  Ali Cinar,et al.  Statistical process monitoring and disturbance diagnosis in multivariable continuous processes , 1996 .

[54]  S. Levinson,et al.  Considerations in dynamic time warping algorithms for discrete word recognition , 1978 .

[55]  W. T. Tucker,et al.  Algorithmic statistical process control: concepts and an application , 1992 .

[56]  Paul Geladi,et al.  An example of 2-block predictive partial least-squares regression with simulated data , 1986 .

[57]  A. J. Morris,et al.  Multivariate Statistical Process Control Applied to an Industrial Production Facility , 1997 .

[58]  J. Edward Jackson,et al.  Principal Components and Factor Analysis: Part I - Principal Components , 1980 .

[59]  Wallace E. Larimore,et al.  Canonical variate analysis in identification, filtering, and adaptive control , 1990, 29th IEEE Conference on Decision and Control.

[60]  Bhavik R. Bakshi,et al.  Analysis of operating data for evaluation, diagnosis and control of batch operations , 1994 .

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

[62]  Age K. Smilde,et al.  Three‐way methods for the calibration of chromatographic systems: Comparing PARAFAC and three‐way PLS , 1991 .

[63]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[64]  Nola D. Tracy,et al.  Multivariate Control Charts for Individual Observations , 1992 .

[65]  Sirish L. Shah,et al.  Practical Issues in Multivariable Feedback Control Performance Assessment , 1997 .

[66]  B. Skagerberg,et al.  Multivariate data analysis applied to low-density polyethylene reactors , 1992 .

[67]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

[68]  Sirish L. Shah,et al.  Good, bad or optimal? Performance assessment of multivariable processes , 1997, Autom..

[69]  L. Tucker,et al.  Some mathematical notes on three-mode factor analysis , 1966, Psychometrika.