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A branch and bound method for isolation of faulty variables through missing variable analysis

By Vinay Kariwala, P. E. Odiowei, Yi Cao and T. Chen

Abstract

Fault detection and diagnosis is a critical approach to ensure safe and efficient operation of manufacturing and chemical processing plants. Although multivariate statistical process monitoring has received considerable attention, investigation into the diagnosis of the source or cause of the detected process fault has been relatively limited. This is partially due to the difficulty in isolating multiple variables, which jointly contribute to the occurrence of fault, through conventional contribution analysis. In this work, a method based on probabilistic principal component analysis is proposed for fault isolation. Furthermore, a branch and bound method is developed to handle the combinatorial nature of problem involving finding the contributing variables, which are most likely to be responsible for the occurrence of fault. The efficiency of the method proposed is shown through benchmark examples, such as Tennessee Eastman process, and randomly generated cases

Topics: Branch and bound Combinatorial optimization Global optimization Multivariate contribution analysis Multivariate statistical process monitoring Principal component analysis principal component analysis gaussian mixture model feature-selection part i algorithm pca
Publisher: Elsevier Science B.V., Amsterdam.
Year: 2010
DOI identifier: 10.1016/j.jprocont.2010.07.007
OAI identifier: oai:dspace.lib.cranfield.ac.uk:1826/4783
Provided by: Cranfield CERES
Journal:

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Citations

  1. (1977). A branch and bound algorithm for feature subset selection. doi
  2. (1993). A more ecient branch and bound algorithm for feature selection. Pattern Recognition, doi
  3. (2003). A review of process fault detection and diagnosis Part I: Quantitative model-based methods. Computers and chemical engineering, doi
  4. (2003). A review of process fault detection and diagnosis Part III: Process history based methods. doi
  5. (2003). An improved branch and bound algorithm for feature selection. doi
  6. (2008). Bidirectional branch and bound for controlled variable selection: Part I. Principles and minimum singular value criterion. doi
  7. (2009). Bidirectional branch and bound for controlled variable selection: Part II. Exact local method for self-optimizing control. doi
  8. (2010). Bidirectional branch and bound for controlled variable selection: Part III. Local average loss minimization. doi
  9. (1998). Contribution plots: a missing link in multivariate quality control.
  10. (2000). Fast branch and bound algorithm in feature selection. doi
  11. (2005). Fault detection based on a maximum-likelihood principal component analysis (PCA) mixture. doi
  12. (2005). Improved branch and bound method for control structure screening. doi
  13. (2004). Improved principal component monitoring of large-scale processes. doi
  14. (1985). Matrix Analysis. doi
  15. (1993). Matrix Computations. The Johns Hopkins
  16. (2008). Multimode process monitoring with Bayesian inference-based Gaussian mixture models. doi
  17. (2005). Multivariate statistical process control using mixture modelling. doi
  18. (2010). Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations. doi
  19. (2005). On-line dynamic process monitoring using wavelet-based generic dissimilarity measure. doi
  20. (2002). Principal Component Analysis. doi
  21. (2009). Probabilistic contribution analysis for statistical process monitoring: A missing variable approach. doi
  22. (1999). Probabilistic principal component analysis. doi
  23. (2006). Probability density estimation via an in Gaussian mixture model: application to statistical process monitoring. doi
  24. (2003). Process monitoring based on probabilistic PCA. Chemometrics and intelligent laboratory systems, doi
  25. (2004). Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis. doi
  26. (2001). Reconstruction based fault identi using a combined index. doi
  27. (2010). State-space independent component analysis for nonlinear dynamic process monitoring. Chemometrics and Intelligent Laboratory Systems, doi
  28. (2004). Statistical monitoring of dynamic processes based on dynamic independent component analysis. doi
  29. (2003). Statistical process monitoring: basics and beyond. doi
  30. (2004). Synthesis of T2 and Q statistics for process monitoring. Control Engineering Practice, doi

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