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Robust kernel distance multivariate control chart using support vector principles

By Fatih Camci, R. B. Chinnam and R. D. Ellis

Abstract

It is important to monitor manufacturing processes in order to improve product quality and reduce production cost. Statistical Process Control (SPC) is the most commonly used method for process monitoring, in particular making distinctions between variations attributed to normal process variability to those caused by ‘special causes’. Most SPC and multivariate SPC (MSPC) methods are parametric in that they make assumptions about the distributional properties and autocorrelation structure of in-control process parameters, and, if satisfied, are effective in managing false alarms/-positives and false- negatives. However, when processes do not satisfy these assumptions, the effectiveness of SPC methods is compromised. Several non-parametric control charts based on sequential ranks of data depth measures have been proposed in the literature, but their development and implementation have been rather slow in industrial process control. Several non-parametric control charts based on machine learning principles have also been proposed in the literature to overcome some of these limitations. However, unlike conventional SPC methods, these non-parametric methods require event data from each out-of-control process state for effective model building. The paper presents a new non-parametric multivariate control chart based on kernel distance that overcomes these limitations by employing the notion of one-class classification based on support vector principles. The chart is non-parametric in that it makes no assumptions regarding the data probability density and only requires ‘normal’ or in-control data for effective representation of an in-control process. It does, however, make an explicit provision to incorporate any available data from out-of-control process states. Experimental evaluation on a variety of benchmarking datasets suggests that the proposed chart is effective for process mo

Topics: Control chart, Support vector machines, Kernel distance
Publisher: Taylor & Francis
Year: 2012
DOI identifier: 10.1080/00207540500543265
OAI identifier: oai:dspace.lib.cranfield.ac.uk:1826/6882
Provided by: Cranfield CERES
Journal:

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Citations

  1. (2004). A class of distribution-free control charts. doi
  2. (1991). A comparison of neural networks to spc charts. Computers and Industrial Engineering, doi
  3. (2003). A kernel-distance-based multivariate control chart using support vector methods. doi
  4. (1996). A markov chain model for the multivariate exponentially weighted moving averages control chart. doi
  5. (1992). A multivariate exponentially weighted moving average control chart. doi
  6. (2004). A nonparametric multivariate control chart based on data depth.
  7. (1993). A quality index based on data depth and multivariate rank tests. doi
  8. (2001). A smooth nonparametric approach to multivariate process capability. doi
  9. (1995). Adequately address abnormal situation operations. Chemical Engineering Progress,
  10. (2001). An introduction to kernel-based learning algorithms. doi
  11. (1992). Automation and the total quality paradigm,
  12. (2004). Design strategies for the multivariate exponentially weighted moving average control chart. doi
  13. (1909). Functions of positive and negative type and their connection with the theory of integral equations. doi
  14. (1991). Mathematics of success and failure. Circuits and Devices, doi
  15. (1998). Multiscale PCA with application to multivariate statistical process monitoring. doi
  16. (2001). Multiscale statistical process control using wavelets: Theoretical analysis and properties. doi
  17. (2001). Multivariate cumulative sum control charts based on projection pursuit. Statistica Sinica,
  18. (2004). Multivariate extensions to cumulative sum control charts. Quality and Reliability Engineering International, doi
  19. (1947). Multivariate quality control-illustrated by the air testing of sample bombsights. Techniques of statistical analysis.
  20. (1951). Nonlinear programming, doi
  21. (2004). Nonlinear programming. doi
  22. (2001). Nonparametric control charts: An overview and some results.
  23. (2001). On shewhart-type nonparametric multivariate control charts based on data depth. Frontiers in Statistical Quality Control, doi
  24. (2001). One class classification. doi
  25. (2004). Principal-component analysis of multiscale data for process monitoring and fault diagnosis. doi
  26. (1996). Process performance monitoring using multivariate statistical process control. doi
  27. (1926). Quality control charts. Bell System Technical Journal, doi
  28. (1999). Research issues and ideas in statistical process control.
  29. (1995). Statistical process control of multivariate processes. Control Engineering Practice, doi
  30. (2003). Statistical process control: What you don't measure can hurt you! doi
  31. (2004). Support vector data description. doi
  32. (1999). Support vector domain description. Pattern Recognition Letters, doi
  33. (2003). Support vector machines for class representation and discrimination, doi
  34. (2002). Support vector machines for recognizing shifts in correlated and other manufacturing processes. doi
  35. (1976). The effect of non-normality on the control limits of X charts.
  36. (1998). Using radial basis function neural networks to recognize shifts in correlated manufacturing process parameters. IIE Transactions, doi
  37. (2000). Wavelet-pls regression models for both exploratory data analysis and process monitoring. doi

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