A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

Abstract This paper provides a comparison study on the basic data-driven methods for process monitoring and fault diagnosis (PM–FD). Based on the review of these methods and their recent developments, the original ideas, implementation conditions, off-line design and on-line computation algorithms as well as computation complexity are discussed in detail. In order to further compare their performance from the application viewpoint, an industrial benchmark of Tennessee Eastman (TE) process is utilized to illustrate the efficiencies of all the discussed methods. The study results are dedicated to provide a reference for achieving successful PM–FD on large scale industrial processes. Some important remarks are finally concluded in this paper.

[1]  Ping Zhang,et al.  On the application of PCA technique to fault diagnosis , 2010 .

[2]  I. Jolliffe.  Principal Component Analysis , 2002 .

[3]  Paul M. Frank,et al.  Issues of Fault Diagnosis for Dynamic Systems , 2010, Springer London.

[4]  N. Lawrence Ricker,et al.  Decentralized control of the Tennessee Eastman Challenge Process , 1996 .

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

[6]  David G. Stork,et al.  Pattern Classification , 1973 .

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

[8]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[9]  S. Joe Qin,et al.  Statistical process monitoring: basics and beyond , 2003 .

[10]  Arthur K. Kordon,et al.  Fault diagnosis based on Fisher discriminant analysis and support vector machines , 2004, Comput. Chem. Eng..

[11]  S. Joe Qin,et al.  Data-driven Fault Detection and Diagnosis for Complex Industrial Processes , 2009 .

[12]  ChangKyoo Yoo,et al.  Statistical process monitoring with independent component analysis , 2004 .

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

[14]  Christos Georgakis,et al.  Disturbance detection and isolation by dynamic principal component analysis , 1995 .

[15]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[16]  U. Kruger,et al.  Dynamic Principal Component Analysis Using Subspace Model Identification , 2005, ICIC.

[17]  Sirkka-Liisa Jämsä Jounela.  Future trends in process automation , 2007, Annu. Rev. Control..

[18]  E. Oja,et al.  Independent Component Analysis , 2013 .

[19]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[20]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[21]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[22]  A. J. Morris,et al.  Non-parametric confidence bounds for process performance monitoring charts☆ , 1996 .

[23]  Q. Peter He,et al.  A New Fault Diagnosis Method Using Fault Directions in Fisher Discriminant Analysis , 2005 .

[24]  Bhupinder S. Dayal,et al.  Improved PLS algorithms , 1997 .

[25]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[26]  I. Helland.  ON THE STRUCTURE OF PARTIAL LEAST SQUARES REGRESSION , 1988 .

[27]  Michel Verhaegen,et al.  Identification of the deterministic part of MIMO state space models given in innovations form from input-output data , 1994, Autom..

[28]  Yingwei Zhang,et al.  Fault detection of non-Gaussian processes based on modified independent component analysis , 2010 .

[29]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[30]  Steven X. Ding,et al.  Data-driven monitoring for stochastic systems and its application on batch process , 2013, Int. J. Syst. Sci..

[31]  S. Qin,et al.  Selection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods† , 1999 .

[32]  Richard D. Braatz,et al.  Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes , 2000 .

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

[34]  Ping Zhang,et al.  Subspace method aided data-driven design of fault detection and isolation systems , 2009 .

[35]  Michel Kinnaert,et al.  Diagnosis and Fault-Tolerant Control , 2006 .

[36]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[37]  Michèle Basseville,et al.  Detection of abrupt changes: theory and application , 1993 .

[38]  Donghua Zhou,et al.  Total projection to latent structures for process monitoring , 2009 .

[39]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[40]  B. Moor,et al.  Subspace identification for linear systems , 1996 .

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

[42]  B. Moor,et al.  Subspace state space system identification for industrial processes , 1998 .

[43]  Sirkka-Liisa Jämsä-Jounela,et al.  Future Trends in Process Automation , 2007 .

[44]  Te-Won Lee,et al.  Independent Component Analysis , 1998, Springer US.

[45]  In-Beum Lee,et al.  Fault detection and diagnosis based on modified independent component analysis , 2006 .

[46]  Leo H. Chiang,et al.  Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis , 2000 .

[47]  Donghua Zhou,et al.  Geometric properties of partial least squares for process monitoring , 2010, Autom..

[48]  James Moyne,et al.  Virtual metrology and feedback control for semiconductor manufacturing processes using recursive partial least squares , 2008 .

[49]  G. Box.  Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification , 1954 .

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

[51]  Steven X. Ding,et al.  Study on modifications of PLS approach for process monitoring , 2011 .

[52]  Nina F. Thornhill,et al.  Advances and new directions in plant-wide disturbance detection and diagnosis , 2007 .

[53]  J. E. Jackson.  A User's Guide to Principal Components , 1991 .

[54]  G. McLachlan.  Discriminant Analysis and Statistical Pattern Recognition , 1992 .

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

[56]  Torsten Jeinsch,et al.  A Survey of the Application of Basic Data-Driven and Model-Based Methods in Process Monitoring and Fault Diagnosis , 2011 .

[57]  Mark A. Girolami,et al.  Self-Organising Neural Networks: Independent Component Analysis and Blind Source Separation , 1999 .