- -

Latent Structures based-Multivariate Statistical Process Control: a paradigm shift

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Latent Structures based-Multivariate Statistical Process Control: a paradigm shift

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Alberto Ferrer's ...
Tamaño: 1.692Mb
Formato: PDF
Descripción: Versión del Autor.
Abrir
[Cerrado]
Nombre: Ferrer_LSb-MSPC-p ...
Tamaño: 889.1Kb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Ferrer, Alberto es_ES
dc.date.accessioned 2016-02-11T12:51:26Z
dc.date.available 2016-02-11T12:51:26Z
dc.date.issued 2014-01-02
dc.identifier.issn 0898-2112
dc.identifier.uri http://hdl.handle.net/10251/60805
dc.description.abstract The basic fundamentals of statistical process control (SPC) were proposed by Walter Shewhart for data-starved production environments typical in the 1920s and 1930s. In the 21st century, the traditional scarcity of data has given way to a data-rich environment typical of highly automated and computerized modern processes. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio, multistage and multiway structures, and missing values. Conventional univariate and multivariate SPC techniques are not suitable in these environments. This article discusses the paradigm shift to which those working in the quality improvement field should pay keen attention. We advocate the use of latent structure based multivariate statistical process control methods as efficient quality improvement tools in these massive data contexts. This is a strategic issue for industrial success in the tremendously competitive global market. es_ES
dc.description.sponsorship This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02. en_EN
dc.language Inglés es_ES
dc.publisher Taylor & Francis es_ES
dc.relation.ispartof Quality Engineering es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Control charts es_ES
dc.subject Latent structures es_ES
dc.subject Multivariate statistical process control (MSPC) es_ES
dc.subject Partial least squares (PLS) es_ES
dc.subject Principal component analysis (PCA) es_ES
dc.subject Quality improvement es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Latent Structures based-Multivariate Statistical Process Control: a paradigm shift es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1080/08982112.2013.846093
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//DPI2011-28112-C04-02/ES/MONITORIZACION, INFERENCIA, OPTIMIZACION Y CONTROL MULTI-ESCALA: DE CELULAS A BIORREACTORES. (MULTISCALES)/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Ferrer, A. (2014). Latent Structures based-Multivariate Statistical Process Control: a paradigm shift. Quality Engineering. 26(1):72-91. https://doi.org/10.1080/08982112.2013.846093 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1080/08982112.2013.846093 es_ES
dc.description.upvformatpinicio 72 es_ES
dc.description.upvformatpfin 91 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 26 es_ES
dc.description.issue 1 es_ES
dc.relation.senia 282468 es_ES
dc.identifier.eissn 1532-4222
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.description.references Aparisi, F., Jabaioyes, J., & Carrion, A. (1999). Statistical properties of the lsi multivariate control chart. Communications in Statistics - Theory and Methods, 28(11), 2671-2686. doi:10.1080/03610929908832445 es_ES
dc.description.references Arteaga, F., & Ferrer, A. (2002). Dealing with missing data in MSPC: several methods, different interpretations, some examples. Journal of Chemometrics, 16(8-10), 408-418. doi:10.1002/cem.750 es_ES
dc.description.references Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: an overview. Quality and Reliability Engineering International, 23(5), 517-543. doi:10.1002/qre.829 es_ES
dc.description.references Bharati, M. H., & MacGregor, J. F. (1998). Multivariate Image Analysis for Real-Time Process Monitoring and Control. Industrial & Engineering Chemistry Research, 37(12), 4715-4724. doi:10.1021/ie980334l es_ES
dc.description.references Bharati, M. H., MacGregor, J. F., & Tropper, W. (2003). Softwood Lumber Grading through On-line Multivariate Image Analysis Techniques. Industrial & Engineering Chemistry Research, 42(21), 5345-5353. doi:10.1021/ie0210560 es_ES
dc.description.references Bisgaard, S. (2012). The Future of Quality Technology: From a Manufacturing to a Knowledge Economy & From Defects to Innovations. Quality Engineering, 24(1), 30-36. doi:10.1080/08982112.2011.627010 es_ES
dc.description.references Box, G. E. P. (1954). Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification. The Annals of Mathematical Statistics, 25(2), 290-302. doi:10.1214/aoms/1177728786 es_ES
dc.description.references Camacho, J., & Ferrer, A. (2012). Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: theoretical aspects. Journal of Chemometrics, 26(7), 361-373. doi:10.1002/cem.2440 es_ES
dc.description.references Duchesne, C., Liu, J. J., & MacGregor, J. F. (2012). Multivariate image analysis in the process industries: A review. Chemometrics and Intelligent Laboratory Systems, 117, 116-128. doi:10.1016/j.chemolab.2012.04.003 es_ES
dc.description.references Efron, B., & Gong, G. (1983). A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation. The American Statistician, 37(1), 36-48. doi:10.1080/00031305.1983.10483087 es_ES
dc.description.references Ferrer, A. (2007). Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process. Quality Engineering, 19(4), 311-325. doi:10.1080/08982110701621304 es_ES
dc.description.references Fuchs, C. (1998). Multivariate Quality Control. doi:10.1201/9781482273731 es_ES
dc.description.references Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1-17. doi:10.1016/0003-2670(86)80028-9 es_ES
dc.description.references Helland, I. S. (1988). On the structure of partial least squares regression. Communications in Statistics - Simulation and Computation, 17(2), 581-607. doi:10.1080/03610918808812681 es_ES
dc.description.references Höskuldsson, A. (1988). PLS regression methods. Journal of Chemometrics, 2(3), 211-228. doi:10.1002/cem.1180020306 es_ES
dc.description.references Hunter, J. S. (1986). The Exponentially Weighted Moving Average. Journal of Quality Technology, 18(4), 203-210. doi:10.1080/00224065.1986.11979014 es_ES
dc.description.references Edward Jackson, J. (1985). Multivariate quality control. Communications in Statistics - Theory and Methods, 14(11), 2657-2688. doi:10.1080/03610928508829069 es_ES
dc.description.references Jackson, J. E., & Mudholkar, G. S. (1979). Control Procedures for Residuals Associated With Principal Component Analysis. Technometrics, 21(3), 341-349. doi:10.1080/00401706.1979.10489779 es_ES
dc.description.references Process analysis and abnormal situation detection: from theory to practice. (2002). IEEE Control Systems, 22(5), 10-25. doi:10.1109/mcs.2002.1035214 es_ES
dc.description.references Kourti, T. (2005). Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19(4), 213-246. doi:10.1002/acs.859 es_ES
dc.description.references Kourti, T. (2006). Process Analytical Technology Beyond Real-Time Analyzers: The Role of Multivariate Analysis. Critical Reviews in Analytical Chemistry, 36(3-4), 257-278. doi:10.1080/10408340600969957 es_ES
dc.description.references Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28(4), 409-428. doi:10.1080/00224065.1996.11979699 es_ES
dc.description.references Liu, R. Y. (1995). Control Charts for Multivariate Processes. Journal of the American Statistical Association, 90(432), 1380-1387. doi:10.1080/01621459.1995.10476643 es_ES
dc.description.references Liu, R. Y., Singh, K., & Teng*, J. H. (2004). DDMA-charts: Nonparametric multivariate moving average control charts based on data depth. Allgemeines Statistisches Archiv, 88(2), 235-258. doi:10.1007/s101820400170 es_ES
dc.description.references Liu, R. Y., & Tang, J. (1996). Control Charts for Dependent and Independent Measurements Based on Bootstrap Methods. Journal of the American Statistical Association, 91(436), 1694-1700. doi:10.1080/01621459.1996.10476740 es_ES
dc.description.references LOWRY, C. A., & MONTGOMERY, D. C. (1995). A review of multivariate control charts. IIE Transactions, 27(6), 800-810. doi:10.1080/07408179508936797 es_ES
dc.description.references MacGregor, J. F. (1997). Using On-Line Process Data to Improve Quality: Challenges for Statisticians. International Statistical Review, 65(3), 309-323. doi:10.1111/j.1751-5823.1997.tb00311.x es_ES
dc.description.references Mason, R. L., Champ, C. W., Tracy, N. D., Wierda, S. J., & Young, J. C. (1997). Assessment of Multivariate Process Control Techniques. Journal of Quality Technology, 29(2), 140-143. doi:10.1080/00224065.1997.11979743 es_ES
dc.description.references Montgomery, D. C., & Woodall, W. H. (1997). A Discussion on Statistically-Based Process Monitoring and Control. Journal of Quality Technology, 29(2), 121-121. doi:10.1080/00224065.1997.11979738 es_ES
dc.description.references Nelson, P. R. C., Taylor, P. A., & MacGregor, J. F. (1996). Missing data methods in PCA and PLS: Score calculations with incomplete observations. Chemometrics and Intelligent Laboratory Systems, 35(1), 45-65. doi:10.1016/s0169-7439(96)00007-x es_ES
dc.description.references Nomikos, P., & MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 37(1), 41-59. doi:10.1080/00401706.1995.10485888 es_ES
dc.description.references Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002 es_ES
dc.description.references Prats-Montalbán, J. M., Ferrer, A., Malo, J. L., & Gorbeña, J. (2006). A comparison of different discriminant analysis techniques in a steel industry welding process. Chemometrics and Intelligent Laboratory Systems, 80(1), 109-119. doi:10.1016/j.chemolab.2005.08.005 es_ES
dc.description.references Prats-Montalbán, J. M., & Ferrer, A. (2007). Integration of colour and textural information in multivariate image analysis: defect detection and classification issues. Journal of Chemometrics, 21(1-2), 10-23. doi:10.1002/cem.1026 es_ES
dc.description.references Bisgaard, S., Doganaksoy, N., Fisher, N., Gunter, B., Hahn, G., Keller-McNulty, S., … Wu, C. F. J. (2008). The Future of Industrial Statistics: A Panel Discussion. Technometrics, 50(2), 103-127. doi:10.1198/004017008000000136 es_ES
dc.description.references Stoumbos, Z. G., Reynolds, M. R., Ryan, T. P., & Woodall, W. H. (2000). The State of Statistical Process Control as We Proceed into the 21st Century. Journal of the American Statistical Association, 95(451), 992-998. doi:10.1080/01621459.2000.10474292 es_ES
dc.description.references Tracy, N. D., Young, J. C., & Mason, R. L. (1992). Multivariate Control Charts for Individual Observations. Journal of Quality Technology, 24(2), 88-95. doi:10.1080/00224065.1992.12015232 es_ES
dc.description.references Wierda, S. J. (1994). Multivariate statistical process control—recent results and directions for future research. Statistica Neerlandica, 48(2), 147-168. doi:10.1111/j.1467-9574.1994.tb01439.x es_ES
dc.description.references Wold, S. (1978). Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models. Technometrics, 20(4), 397-405. doi:10.1080/00401706.1978.10489693 es_ES
dc.description.references Woodall, W. H. (2000). Controversies and Contradictions in Statistical Process Control. Journal of Quality Technology, 32(4), 341-350. doi:10.1080/00224065.2000.11980013 es_ES
dc.description.references Woodall, W. H., & Montgomery, D. C. (1999). Research Issues and Ideas in Statistical Process Control. Journal of Quality Technology, 31(4), 376-386. doi:10.1080/00224065.1999.11979944 es_ES
dc.description.references Yu, H., & MacGregor, J. F. (2003). Multivariate image analysis and regression for prediction of coating content and distribution in the production of snack foods. Chemometrics and Intelligent Laboratory Systems, 67(2), 125-144. doi:10.1016/s0169-7439(03)00065-0 es_ES
dc.description.references Yu, H., MacGregor, J. F., Haarsma, G., & Bourg, W. (2003). Digital Imaging for Online Monitoring and Control of Industrial Snack Food Processes. Industrial & Engineering Chemistry Research, 42(13), 3036-3044. doi:10.1021/ie020941f es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem