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Quality Control with R pp 271–284Cite as

Nonlinear Profiles with R

Part of the Use R! book series (USE R)

Abstract

In many situations, processes are often represented by a function that involves a response variable and a number of predictive variables. In this chapter, we show how to treat data whose relation between the predictive and response variables is nonlinear and, as a consequence, cannot be adequately represented by a linear model. This kind of data are known as nonlinear profiles. Our aim is to show how to build nonlinear control limits and a baseline prototype using a set of observed in-control profiles. Using R, we show how to afford situations in which nonlinear profiles arise and how to plot easy-to-use nonlinear control charts.

Keywords

  • Nonlinear Profile
  • Control Charts
  • Baseline Prototype
  • Predictor Variables
  • Response Variables

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Boeing Commercial Airplane Group, M.D.P.Q.A.D.: Advanced Quality System Tools, AQS D1-9000-1. Toolbox (1998). url http://www.boeingsuppliers.com/supplier/d1-9000-1.pdf

  2. Cano, E.L., Moguerza, J.M., Redchuk, A.: Six sigma with R. Statistical Engineering for Process Improvement, Use R!, vol. 36. Springer, New York (2012). url http://www.springer.com/statistics/book/978-1-4614-3651-5

  3. Cano, J., Moguerza, J.M., Psarakis, S., Yannacopoulos, A.N.: Using statistical shape theory for the monitoring of nonlinear profiles. Appl. Stoch. Model. Bus. Ind. 31(2), 160–177 (2015). doi:10.1002/asmb.2059. url http://dx.doi.org/10.1002/asmb.2059

  4. ISO TC69/SC4–Applications of statistical methods in process management: ISO 7870-2:2013 - Control charts – Part 2: Shewhart control charts. Published standard (2013). url http://www.iso.org/iso/catalogue_detail.htm?csnumber=40174

  5. ISO TC69/SC4–Applications of statistical methods in process management: ISO 7870-5:2014 - Control charts – Part 5: Specialized control charts. Published standard (2014). url http://www.iso.org/iso/catalogue_detail.htm?csnumber=40177

  6. ISO TC69/SC6–Measurement methods and results: ISO 11843-5:2008 - Capability of detection – Part 5: Methodology in the linear and non-linear calibration cases. Published standard (2012). url http://www.iso.org/iso/catalogue_detail.htm?csnumber=42000

  7. ISO/TC 108, Mechanical vibration, shock and condition monitoring, Subcommittee SC 5, Condition monitoring and diagnostics of machines: Condition monitoring and diagnostics of machines – Data interpretation and diagnostics techniques – Part 1: General guidelines. Published standard (2012). url http://www.iso.org/iso/home/store/catalogue_tc/catalogue_detail.htm?csnumber=39836

  8. ISO/TC 184, Automation systems and integration, Subcommittee SC 5, Interoperability, integration and architectures of automation systems and applications: Automation systems and integration – Integration of advanced process control and optimization capabilities for manufacturing systems – Part 1: Framework and functional model. Published standard (2015). url http://www.iso.org/iso/catalogue_detail.htm?csnumber=61131

  9. Moguerza, J., Muñoz, A., Psarakis, S.: Monitoring nonlinear profiles using support vector machines. In: Rueda, L., Mery, D., Kittler, J. (eds.) Progress in Pattern Recognition, Image Analysis and Applications. Lecture Notes in Computer Science, vol. 4756, pp. 574–583. Springer, Heidelberg (2007). doi:10.1007/978-3-540-76725-1_60. url http://dx.doi.org/10.1007/978-3-540-76725-1_60

  10. Moguerza, J.M., Muñoz, A.: Support vector machines with applications. Stat. Sci. 21(3), 322–336 (2006)

    CrossRef  MathSciNet  MATH  Google Scholar 

  11. Walker, E., Wright, W.: Comparing curves with additive models. J. Qual. Technol. 34(1), 118–129 (2002)

    Google Scholar 

  12. Woodall, W.H.: Current research in profile monitoring. Producão 17(3), 420–425 (2007). Invited paper

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Statistics, Rey Juan Carlos University, Madrid, Spain

    Emilio L. Cano & Javier M. Moguerza

  2. Statistics Area, DHEP, The University of Castilla-La Mancha, Ciudad Real, Spain

    Emilio L. Cano

  3. ENUSA Industrias Avanzadas, Madrid, Spain

    Mariano Prieto Corcoba

Authors
  1. Emilio L. Cano
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  2. Javier M. Moguerza
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  3. Mariano Prieto Corcoba
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Cano, E.L., Moguerza, J.M., Corcoba, M.P. (2015). Nonlinear Profiles with R. In: Quality Control with R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-24046-6_10

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