Skip to main content
SpringerLink
Log in

Multivariate Process Monitoring for Nylon Fiber Production

  • Conference paper
Frontiers in Statistical Quality Control 6

Part of the book series: Frontiers in Statistical Quality Control ((FSQC,volume 6))

  • 475 Accesses

Abstract

From the 1930s, literature on SPC schemes have been “captured” by the Shewhart paradigm of normality, independence and homogeneous variance. When in fact, the problems facing today’s industries are more inconsistent than those faced by Dr. Shewhart in the 1930s. As a result of the advances in machine and sensor technology, process data can often be collected on-line. In this situation, the process observations that result from data collection activities will frequently not be independent, but autocorrelated. Autocorrelation has a significant impact on a statistical control chart: the process may not exhibit a state of statistical control when in fact, it is in control. As the prevalence of this type of data is expected to increase in industry, so does the need to control and monitor it. A second consequence of this technology shift is that many industries now operate in a data-rich environment. They have the ability to gather many observations on several quality characteristics in a short period of time. This gives rise to a situation probably not envisioned by Shewhart in the 1930s: too much process data and too little information about the process. This domain is ripe for the methods of data mining and multivariate quality control. This paper develops a multivariate statistical process monitoring scheme for a continuous process: the production of mono-filament nylon fibers. The data is abundant (over 80 process and quality variables) and has varying degrees of serial correlation. Various data mining methodologies are used to determine the driving variables which affect the response variable, spin breaks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alt, F.B. (1985) Multivariate Quality Control. Encyclopedia of Statistical Sciences 6 (S. Kotz and N. Johnson, eds.), 110–122. Wiley & Sons, New York

    Google Scholar 

  2. Alwan, L. C. (1991) Autocorrelation: Fixed Versus Variable Control Limits. Quality Engineering 4(2) 167–188

    Article  Google Scholar 

  3. Alwan, L. C. (1992) Effects of Autocorrelation on Control Chart Performance. Communications in Statistics: Theory and Methods 21 1025–1049

    Article  MATH  Google Scholar 

  4. Alwan, L. C. and H. V. Robert (1988) Time Series Modeling for Statistical Process Control. Journal of Business & Economic Statistics 6(1) 87–95

    Google Scholar 

  5. Barron, A.R. (1984) Predicted Square Error: A Criterion for Automatic Model Selection, in Self-Organizing Methods in Modeling: GMDH type Algorithms. Marcel Dekker, New York

    Google Scholar 

  6. Box, G. E. P., G. M. Jenkins and G.C. Reinsel (1994) Time Series Analysis, Forecasting and Control. 3rd Edition, Prentice Hall

    MATH  Google Scholar 

  7. Brown, D.E. and J.F. Elder (1996) Induction and Polynomial Networks, Internal Report. Institute for Parallel Computation, Department of Systems Engineering, University of Virginia

    Google Scholar 

  8. Chames, J.M. (1995) Tests for Special Causes with Multivariate Autocorrelated Data. Computers in Operations Research 22(4) 443–453

    Article  Google Scholar 

  9. Farlow, S.J. (1984) Self-Organizing Methods in Modeling: GMDH type Algorithms. Marcel Dekker, New York

    MATH  Google Scholar 

  10. Hahn, G.J. (1989) Statistics-Aided Manufacturing: A Look Into the Future. American Statistician 43 74–79

    Google Scholar 

  11. Harris, T. J. and W. H. Ross (1991) Statistical Process Control Procedures for Correlated Observations. The Canadian Journal of Chemical Engineering 69 48–57

    Article  Google Scholar 

  12. Jackson, J.E. (1980) Principal Components and Factor Analysis: Part 1—Principal Components. Journal of Quality Technology 12 201–213

    Google Scholar 

  13. Kourti, T. and J.F. MacGregor (1996) Multivariate SPC Methods for Process and Production Monitoring. Journal Quality Technology 28 409–428

    Google Scholar 

  14. Kramer, H.G. and W. Schmid (1997) EWMA Charts for Multivariate Time Series. Sequential Anlaysis 16(2) 131–154

    Article  MATH  MathSciNet  Google Scholar 

  15. Kresta, J.V., MacGregor, J.F., and Marlin, T.E. (1991) Multivariate Statistical Monitoring of Process Operating Performance. Canadian Journal of Chemical Engineering 69 35–47

    Article  Google Scholar 

  16. Lowry, C.A. and D.C. Montgomery (1995) A Review of Multivariate Control Charts. IIE Transactions 27 800–810

    Article  Google Scholar 

  17. Lowry, C. A., W. H. Woodall, C. W. Champ and S. E. Rigdon (1992) A Multivariate Exponentially Weighted Moving Average Control Chart. Technometrics 34(1) 46–53

    Article  MATH  Google Scholar 

  18. Lu, C. and M.R. Reynolds: Control Charts for Monitoring the Mean and Variance of Autocorrelated Processes, to appear in Journal of Quality Technology

    Google Scholar 

  19. Mason, R.L., C.W. Champ, N.D. Tracy, S.J. Wierda, and J.C. Young (1997) Assessment of Multivariate Process Control Techniques. Journal of Quality Technology 29(2) 140–143

    Google Scholar 

  20. Mason, R.L, N.D. Tracy, and J.C. Young (1996) Monitoring a Multivariate Step Process. Journal of Quality Technology 28(1) 39–50

    Google Scholar 

  21. Mastrangelo, C.M. and D.C. Montgomery (1995) Characterization of a Moving Centerline Exponentially Weighted Moving Average. Quality and Reliability Engineering International 11(2) 79–89

    Article  Google Scholar 

  22. Mastrangelo, C.M., G.C. Runger, and D.C. Montgomery (1996) Statistical Process Monitoring with Principal Components. Quality and Reliability Engineering International 12(3)

    Article  Google Scholar 

  23. ModelQuest Enterprise User’s Manual (1998) AbTech Corporation, Charlottesville, VA

    Google Scholar 

  24. Montgomery, D. C. and D. J. Friedman (1989) Statistical Process Control in a Computer-Integrated Manufacturing Environment, in Statistical Process Control in Automated Manufacturing, eds. J.B. Keats and N.F. Hubele. Marcel Dekker, New York

    Google Scholar 

  25. Montgomery, D.C. and C.M. Mastrangelo (1991) Some Statistical Process Control Methods for Autocorrelated Data. Journal of Quality Technology 23(3) 179–193

    Google Scholar 

  26. Montgomery, D.C. and E.A. Peck (1982) Introduction to Linear Regression Analysis. Wiley, New York

    MATH  Google Scholar 

  27. Murphy, B.J. (1987) Selecting Out of Control Variables with the T2 Multivariate Quality Control Procedure. The Statistician 36 571–583

    Article  Google Scholar 

  28. Neter J., W. Wasserman and M.H. Kutner (1990) Applied Staitistical Linear Models: Regression, Analysis of Variance and Experimental Design

    Google Scholar 

  29. Prabhu, S.S. and G.C. Runger (1997) Designing a Mulivariate EWMA Control Chart. Journal of Quality Technology 29(1) 8–15

    Google Scholar 

  30. Runger, G.C. (1996) Multivariate Statistical Process Control for Autocorrelated Processes. International Journal of Production Research 34(6) 1715–1724

    Article  MATH  MathSciNet  Google Scholar 

  31. Runger, G.C. and F.B. Alt (1996) Contributors to a Multivariate Statistical Process Control Chart Signal. Communications in Statistics—Theory and Methods 25(10) 2203–2213

    MATH  MathSciNet  Google Scholar 

  32. Runger, G.C. and T.R. Willemain (1995) Model-Based and Model-Free Control of Autocorrelated Processes. Journal of Quality Technology 27 283–292

    Google Scholar 

  33. Schmid, W. (1995) On the Run Length of a Shewhart Chart for Correlated Data. Statistical Papers 36 111–130

    Article  MATH  MathSciNet  Google Scholar 

  34. Sullivan J.H. and W.H. Woodall (1996) A Comparison of Multivariate Control charts for Individual Observations. Journal of Quality Technology 28(4) 398

    Google Scholar 

  35. Therrien, C.W. (1989) Decision, Estimation and Classification. Wiley, New York

    MATH  Google Scholar 

  36. Tracy, N.D., J.C. Young, and R.L. Mason (1992) Multivariate Control Charts for Individual Observations. Journal of Quality Technology 24(2) 88–95

    Google Scholar 

  37. VanBrackle, L.N. and M.R. Reynolds (1997) EWMA and CUSUM Control Charts in the Persence of Correlattion, Communications in Statistics—Simulation 26(3) 979–1008

    Article  MATH  MathSciNet  Google Scholar 

  38. Wardell, D. G., H. Moskowitz and R. D. Plante (1992) Control Charts in the Presence of Data Correlation. Management Science 38(8) 1084–1105

    Article  MATH  Google Scholar 

  39. Wardell, D.G., H. Moskowitz, and R.D. Plante (1994) Run-Length Distributions of Special-Cause Control Charts for Correlated Processes and Discussion. Technometrics 36(1) 3–27

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Systems Engineering, University of Virginia, VA, 22903, Charlottesville

    Christina M. Mastrangelo

  2. Proctor & Gamble Company, Cincinnati, OH, 45202

    Joseph M. Porter

  3. Solutia, Inc., Pensacola, FL, 32560

    Robert V. Baxley Jr.

Authors
  1. Christina M. Mastrangelo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joseph M. Porter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robert V. Baxley Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institut für Statistik und Ökonometrie, Freie Universität Berlin, Garystraße 21, Berlin, 14195, Germany

    Hans-Joachim Lenz  & Peter-Theodor Wilrich  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mastrangelo, C.M., Porter, J.M., Baxley, R.V. (2001). Multivariate Process Monitoring for Nylon Fiber Production. In: Lenz, HJ., Wilrich, PT. (eds) Frontiers in Statistical Quality Control 6. Frontiers in Statistical Quality Control, vol 6. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57590-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-57590-7_14

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1374-6

  • Online ISBN: 978-3-642-57590-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation