Abstract
In the first stage of a manufacturing process a large number of variables might be available. Then, a smaller number of measurements should be selected for process monitoring. At this point in time, variable selection methods for process monitoring have focused mainly on explained variance performance criteria. However, explained variance efficiency is a minimal notion of optimality and it does not necessarily result in an economically desirable selected subset, as it makes no statement about the measurement cost or other engineering criteria. Without measuring cost many decisions will be impossible to make. In this article, we propose two new methods to select a reduced number of relevant variables for multivariate statistical process control that makes use of engineering, cost and variability evaluation criteria. In the first method we assume that a two-class system is used to classify the variables as primary and secondary based on different criteria. Then a double reduction of dimensionality is applied to select relevant primary variables that represent well the whole set of variables. In the second methodology a cost-utility analysis is used to compare different variable subsets that may be used for process monitoring. The objective of carrying out a cost–utility analysis is to compare one use of resources with other possible uses. To do this, to any process monitoring procedure is assigned a score calculated as ratio of the cost at which it might be obtained to explained variance that it might provide. The subset of relevant variables is selected in a manner that retains, to some extent, the structure and information carried by the full set of original variables.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Colosimo, B.M., Semeraro, Q., Pacella, M.: Statistical process control for geometric specifications: on the monitoring of roundness profiles. J. Qual. Technol. 40, 1–18 (2008)
Woodall, W.H., Spitzner, D.J., Montgomery, D.C., Gupta, S.: Using control charts to monitor process and product quality profiles. J. Qual. Technol. 36, 309–320 (2004)
Gonzalez, I., Sanchez, I.: Variable selection for multivariate statistical process control. J. Qual. Technol. 42(3), 242–259 (2010)
Jackson, J.E.: A User’s Guide in Principal Components. Wiley, New York (1991)
Sullivan, J.H., Woodall, W.H.: A comparison of multivariate control charts for individual observations. J. Qual. Technol. 28(4), 398–408 (1996)
Woodall, W.H., Montgomery, D.C.: Research issues and ideas in statistical process control. J. Qual. Technol. 31(4), 376–386 (1999)
Mason, R.L., Chou, Y.-M., Sullivan, J.H., Stroumbos, Z.G., Young, J.C.: Systematic patterns in T2 charts. J. Qual. Technol. 35(1), 47–58 (2003)
Mason, R.L., Chou, Y.M., Young, J.C.: Detection and interpretation of a multivariate signal using combined charts. Commun. Stat. Theory Methods 40, 942–957 (2011)
Mason, R.L., Tracy, N.D., Young, J.C.: Decomposition for multivariate control chart interpretation. J. Qual. Technol. 27(2), 99–108 (1995)
Kourti, T., MacGregor, J.F.: Multivariate SPC methods for process and product monitoring. J. Qual. Technol. 28, 409–428 (1996)
Kourti, T., Nomikos, P., MacGregor, J.F.: Analysis, monitoring and fault diagnosis of batch processes using multiblock and multi-way PLS. J. Process Control 5, 277–284 (1995)
Jaupi, L.: Multivariate control charts for complex processes. In: Lauro, C., Antoch, J., Esposito, V., Saporta, G. (eds.) Multivariate Total Quality Control, pp. 125–136. Springer Physica Verlag Heidelberg (2001)
García-Muñoz, S., Kourti, T., MacGregor, J.F., Mateos, A.G., Murphy, G.: Troubleshooting of an industrial batch process using multivariate methods. Ind. Eng. Chem. Res. 42, 3592–3601 (2003)
Ferrer, A.: Multivariate statistical process control based on principal component analysis (MSPC-PCA): some reflections and a case study in an autobody assembly process. Qual. Eng. 19(4), 311–325 (2007)
Jolliffe, I.T.: Discarding variables in a principal component analysis I: artificial data. Appl. Stat. 21, 160–173 (1972)
Jolliffe, I.T.: Principal Components Analysis, 2nd edn. Springer, New York (2002)
Krzanowski, W.: Selection of variables to preserve multivariate data structure, using principal components. Appl. Stat. 26, 22–33 (1987)
RAO, C.R.: The use and interpretation of principal components in applied research. Sankhya, A 26, 329–358 (1964)
Cadima, J.F.C.L., Jolliffe, I.T.: Variable selection and the interpretation of principal subspaces. J. Agric. Biol. Environ. Stat. 6, 62–79 (2001)
Cumming, J.A., Wooff, D.A.: Dimension reduction via principal variables. Comput. Stat. Data Anal. 52, 550–565 (2007)
Jolliffe, I.T.: Discarding variables in a principal component analysis II: real data. Appl. Stat. 22, 21–31 (1973)
McCabe, G.P.: Principal variables. Technometrics 26, 137–144 (1984)
Tanaka, Y., Mori, Y.: Principal component analysis based on a subset of variables: variable selection and sensitivity analysis. Am. J. Math. Manag. Sci. 17(1 & 2), 61–89 (1997)
Al-Kandari, N.M., Jolliffe, I.T.: Variable selection and interpretation of covariance principal components. Commun. Stat. Simul. Comput. 30, 339–354 (2001)
Al-Kandari, N.M., Jolliffe, I.T.: Variable selection and interpretation in correlation principal components. Environmetrics 16, 659–672 (2005)
Hampel, F.R., Ronchetti, E.M., Rousseew, P.J., Stahel, W.A.: Robust Statistics – The Approach Based on Influence Functions. Wiley, New-York (1986)
Jaupi, L., Saporta, G.: Using the influence function in robust principal components analysis. In: Morgenthaler, S., Ronchetti, E., Stahel, W.A. (eds.) New Directions in Statistical Data Analysis and Robustness, pp. 147–156. Birkhäuser Verlag, Basel (1993)
Jaupi, L., Herwindiati, D., Durand, P., Ghorbanzadeh, D.: Short run multivariate control charts for process mean and variability. In: Lecture notes in engineering and computer science: proceedings of the world congress on engineering 2013, WCE 2013, pp. 670–674. London, 3–5 July 2013
Jaupi, L., Durand, P., Ghorbanzadeh, D., Herwindiati, D.E.: Multi-criteria variable selection for process monitoring. In: 59th world statistical congress, pp. 3550–3555. Hong Kong, Aug 2013
Jaupi, L.: Variable selection methods for multivariate process monitoring. In: Lecture notes in engineering and computer science: proceedings of the world congress on engineering 2014, WCE 2014, pp. 572–576. London, 2–4 July 2014
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Jaupi, L. (2015). Variable Selection Methods for Process Monitoring. In: Yang, GC., Ao, SI., Gelman, L. (eds) Transactions on Engineering Technologies. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9804-4_29
Download citation
DOI: https://doi.org/10.1007/978-94-017-9804-4_29
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-017-9803-7
Online ISBN: 978-94-017-9804-4
eBook Packages: EngineeringEngineering (R0)