Monitoreo de perfiles no lineales con dependencia temporal en fase II desde un enfoque del análisis de datos funcionales

dc.contributor.advisorGuevara Gonzáles, Ruben Darío
dc.contributor.advisorCalderón Villanueva, Sergio Alejandro
dc.contributor.authorCardenas Pineda, David Humberto
dc.date.accessioned2021-09-16T15:01:04Z
dc.date.available2021-09-16T15:01:04Z
dc.date.issued2020
dc.description.abstractEn el control estadístico de procesos caracterizados por una relación funcional entre dos variables, el supuesto de independencia entre las observaciones de un mismo perfil o entre perfiles es de uso recurrente en una gran cantidad de aplicaciones. La rápida obtención de información, la inercia de los procedimientos, entre otras causas, propician la violación del anterior supuesto, causando que una proporción considerable de los esquemas de control típicos se presenten como inadecuados. En este trabajo se plantea una propuesta de control para el monitoreo de perfiles no lineales en fase II, vistos como realizaciones de procesos temporales estacionarios en espacios funcionales, mediante un enfoque desde el análisis de datos funcionales. A través de un estudio de simulación el desempeño de la propuesta se evalúa. Además, se ilustra su aplicación usando datos industriales para el monitoreo de perfiles de temperatura en hornos industriales. (Texto tomado de la fuente)spa
dc.description.abstractIn the statistical control of processes characterized by functional relationships between two variables the independence assumption of observations within a profile or between profiles is commonly used in most of applications. Flows of data at higher speeds, procedures inertia, among other causes, leads to a violation of the former assumption, driving a considerable amount of control schemes to be classified as inadequate. In this thesis, a control schema is proposed for phase II monitoring of nonlinear profiles, treated as realizations of stationary functional processes, using a functional data analysis approach. Through simulation studies the proposal performance is accessed, furthermore, its use is explained within an industrial application in profile monitoring of industrial ovens temperature.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ciencias - Estadísticaspa
dc.format.extentxv, 87 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/80213
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Estadísticaspa
dc.publisher.facultyFacultad de Cienciasspa
dc.publisher.placeBogotá - Colombiaspa
dc.publisher.programBogotá - Ciencias - Maestría en Ciencias - Estadísticaspa
dc.relation.referencesAlshraideh, H. & Runger, G. (2014), “Process monitoring using hidden markov models”, Quality and Reliability Engineering International 30(8), 1379-1387.spa
dc.relation.referencesAlwan, L. C. & Roberts, H. V. (1988), “Time-series modeling for statistical process control”, Journal of Business and Economic Statistics 6(1), 87-95.spa
dc.relation.referencesAtienza, O. O., Tang, L. C. & Ang, B. W. (2002), “A CUSUM scheme for autocorrelated observations”, Journal of Quality Technology 34(2), 187-199. URL: https://doi.org/10.1080/00224065.2002.11980145spa
dc.relation.referencesAue, A., Norinho, D. D. & Hörmann, S. (2015), “On the prediction of stationary functional time series”, Journal of the American Statistical Association 110(509), 378-392. URL: https://www.tandfonline.com/doi/abs/10.1080/01621459.2014.909317spa
dc.relation.referencesBosq, D. (2000), Linear Processes in Function Spaces Theory and Applications, Springer.spa
dc.relation.referencesBox, G. E. P. & Jenkins, G. M. (1976), Time Series Analysis Forecasting and Control., Holden-Day series in time series analysis and digital processing, Holden-Day.spa
dc.relation.referencesChakraborti, S. (2000), “Run length, average run length and false alarm rate of shewhart x-bar chart: Exact derivations by conditioning”, Communications in Statistics Part B: Simulation and Computation 29(1), 61-81. URL: https://doi.org/10.1080/03610910008813602spa
dc.relation.referencesChakraborti, S. (2007), “Run length distribution and percentiles: The shewhart X-bar chart with unknown parameters”, Quality Engineering 19(2), 119-127. URL: https://doi.org/10.1080/08982110701276653spa
dc.relation.referencesChamp, C. W. & Woodall, W. H. (1987), “Exact results for shewhart control charts with supplementary runs rules”, Technometrics 29(4), 393-399. URL: https://www.tandfonline.com/doi/abs/10.1080/00401706.1987.10488266spa
dc.relation.referencesCheng, T., Hsieh, P. & Yang, S. (2014), “Process Control for the Vector Autoregressive Model”, Quality and Reability Engieneering International 30, 57-81spa
dc.relation.referencesChiang, J. Y., Lio, Y. L. & Tsai, T. R. (2017), “MEWMA Control Chart and Process Capability Indices for Simple Linear Profiles with Within-profile Autocorrelation”, Quality and Reliability Engineering International 33(5), 1083-1094.spa
dc.relation.referencesChuang, S. C., Hung, Y. C., Tsai, W. C. & Yang, S. F. (2013), “A framework for nonparametric profile monitoring”, Computers and Industrial Engineering 64(1), 482-491. URL: http://www.sciencedirect.com/science/article/pii/S0360835212002057spa
dc.relation.referencesDawod, A., Riaz, M. & Abbasi, S. (2017), “On model selection for autocorrelated processes in statistical process control”, Quality Reliability Engineering International 33, 867-882.spa
dc.relation.referencesDe Ketelaere, B., Rato, T., Schmitt, E. & Hubert, M. (2016), “Statistical process monitoring of time-dependent data”, Quality Engineering 28(1), 127-142.spa
dc.relation.referencesDidericksen, D., Kokoszka, P. & Zhang, X. (2012), “Empirical properties of forecast with the functional autoregressive model”, Comput Stat 27(2), 285-298.spa
dc.relation.referencesFaltin, W., Mastrangelo, C., Runger, G. & Ryan, T. (1997), “Considerations in monitoring of autocorrelated and independent data”, Journal of Quality Technology 29(2), 131-133.spa
dc.relation.referencesFraiman, R. & Muniz, G. (2001), “Trimmed means for functional data”, Test 10(2), 419- 440.spa
dc.relation.referencesGabrys, R. & Kokoszka, P. (2007), “Portmanteau test of independence for functional observations”, Journal of the American Statistical Association 102(480), 1338-1348.spa
dc.relation.referencesGarthoff, R. & Schmid, W. (2015), “Control charts for multivariate nonlinear time series”, REVSTAT 13(2), 131-144.spa
dc.relation.referencesGarthoff, R. & Schmid, W. (2017), “Monitoring means and covariances of multivariate non linear time series with heavy tails”, Communications in Statistics - Theory and Methods 46(21), 10394-10415. URL: https://doi.org/10.1080/03610926.2015.1085567spa
dc.relation.referencesGohberg, Y., Goldberg, S. & Kaashoek, M. A. (1993), Classes of linear operators: advances and applications, Vol. 59, Birkhäuser.spa
dc.relation.referencesGoma, A. & Birch, J. (2019), “A semiparametric nonlinear mixed model approach to phase I profiles monitoring”, Communications in Statistics - Simulation and Computation 48(6), 1677-1693.spa
dc.relation.referencesHaridy, S. & Zhang, W. (2009), “Univariate and multiariate control charts for monitoring dynamic-behavior processes: a case study”, Journal of Industrial Engineering and Management 2(3), 464-498.spa
dc.relation.referencesHauck, D. J., Runger, G. C. & Montgomery, D. C. (1999), “Multivariate statistical process monitoring and diagnosis with grouped regression-adjusted variables”, Communications in Statistics Part B: Simulation and Computation 28(2), 309-328. URL: https://www.tandfonline.com/doi/abs/10.1080/03610919908813551spa
dc.relation.referencesHörmann, S., Kidzi, L. & Hallin, M. (2015), “Dynamic functional principal components”, Journal of the Royal Statistical Society. Series B: Statistical Methodology 77(2), 319- 348. URL: http://doi.wiley.com/10.1111/rssb.12076spa
dc.relation.referencesHörmann, S. & Kidzinski, L. (2015), “A note on estimation in hilbertian linear models”, Scandinavian Journal of Statistics 42(1), 43-62. URL: http://doi.wiley.com/10.1111/sjos.12094spa
dc.relation.referencesHörmann, S. & Kokoszka, P. (2010), “Weakly dependent functional data”, Annals of Sta- tistics 38(3), 1845-1884. URL: https://projecteuclid.org/euclid.aos/1269452656spa
dc.relation.referencesHorváth, L. & Kokoszka, P. (2012), Inference for Functional Data with Applications, Springer. Horváth, L., Kokoszka, P. & Rice, G. (2014), “Testing stationarity of functional time series”, Journal of Econometrics 179(1), 66-82. URL: http://www.sciencedirect.com/science/article/pii/S0304407613002327spa
dc.relation.referencesHuman, S. W., Kritzinger, P. & Chakraborti, S. (2011), “Robustness of the EWMA control chart for individual observations”, Journal of Applied Statistics 38(10), 2071-2087. URL: https://doi.org/10.1080/02664763.2010.545114spa
dc.relation.referencesHyndman, R. J. & Shahid Ullah, M. (2007), “Robust forecasting of mortality and fertility rates: A functional data approach”, Computational Statistics and Data Analysis 51(10), 4942-4956.spa
dc.relation.referencesJarrett, J. E. & Pan, X. (2007), “The quality control chart for monitoring multivariate autocorrelated processes”, Computational Statistics and Data Analysis 51(8), 3862-3870.spa
dc.relation.referencesJensen, W. A. & Birch, J. B. (2011), Correlation and Autocorrelation in Profiles, in R. Noorossana, A. Saghaei & A. Amiri, eds, “Statistical Analysis of Profile Monitoring”, Wiley Series in Probability and Statistics, Jhon Wiley & Sons, Inc, chapter 9, pp. 253-268.spa
dc.relation.referencesJensen, W. & Birch, J. (2009), “Profile monitoring via nonlinear mixed models”, Journal of Quality Technology 41(1), 18-34.spa
dc.relation.referencesJensen, W., Birch, J. & Woodal, W. (2008), “Monitoring correlation within linear profiles using mixed models”, Journal of Quality Technology 40(2), 167-183.spa
dc.relation.referencesJiang, W., Tsui, K. L. & Woodall, W. H. (2000), “A new SPC monitoring method: The ARMA chart”, Technometrics 42(4), 399-410. URL: http://www.jstor.org/stable/1270950spa
dc.relation.referencesKazemzadeh, R., Noorossana, R. & Amiri, A. (2010), “Phase II Monitoring of Autocorrelated Polynomial Profiles in AR(1) Processes”, Scientia Iranica 17(1), 12-24.spa
dc.relation.referencesKhedmati, M. & Niaki, S. T. A. (2016), “Phase II monitoring of general linear profiles in the presence of between-profile autocorrelation”, Quality and Reliability Engineering International 32(2), 443-452.spa
dc.relation.referencesKokoszka, P. & Reimherr, M. (2017), Introduction to functional data analysis, CRC Press.spa
dc.relation.referencesKokoszka, P. & Zhang, X. (2012), “Functional prediction of intraday cumulative returns”, Statistical Modelling 12(4), 377-398. URL: http://journals.sagepub.com/doi/10.1177/1471082X1201200404spa
dc.relation.referencesKramer, H. G. & Schmid, L. V. (1997), “Ewma charts for multivariate time series”, Se- quential Analysis 16(2), 131-154. URL: https://doi.org/10.1080/07474949708836378spa
dc.relation.referencesKunsch, H. R. (1989), “The Jackknife and the Bootstrap for General Stationary Observations”, The Annals of Statistics 17(3), 1217-1241. URL: https://doi.org/10.1214/aos/1176347265spa
dc.relation.referencesLawler, G. F. (2006), Introduction to Stochastic Processes, 2 edn, Taylor and Francis/CRC Press.spa
dc.relation.referencesLi, Y., Huang, M. & Pan, E. (2018), “Residual chart with hidden Markov model to monitoring the auto-correlated processes”, Journal of Shanghai Jiaotong University (Science) 83(Suppl 1), 103-108.spa
dc.relation.referencesLi, Y., Pan, E. & Xiao, Y. (2020), “On autoregressive model selection for the exponentially weighted moving average control chart of residuals in monitoring the mean of autocorrelated processes”, Quality and Reliability Engineering International 36(7), 2351-2369.spa
dc.relation.referencesMacgregor, J. & Harris, T. (1993), “The Exponentially Weighted Moving Variance”, Jour- nal of Quality Technology 25(2), 106-118.spa
dc.relation.referencesMaleki, M. R., Amiri, A. & Castagliola, P. (2018), “An overview on recent profile monitoring papers (2008-2018) based on conceptual classification scheme”, Computers & Industrial Engineering 126, 705-728.spa
dc.relation.referencesMingoti, S. A., de Carvalho, J. P. & de Oliveira Lima, J. (2008), “On the estimation of serial correlation in Markov-dependent production processes”, Journal of Applied Statistics 35(7), 763-771. URL: https://doi.org/10.1080/02664760802005688spa
dc.relation.referencesMontgomery, D. (2013), Introduction to statistical quality control, 7 edn, John Wiley & Sons.spa
dc.relation.referencesMontgomery, D. C. & Mastrangelo, C. M. (1991), “Some Statistical Process Control Methods for Autocorrelated Data”, Journal of Quality Technology 23(3), 179-193.spa
dc.relation.referencesNoorossana, R., Amiri, A. & Soleimani, P. (2008), “On the Monitoring of Autocorrelated Linear Profiles”, Communications in Statistics|Theory and Methods 37(3), 425-442.spa
dc.relation.referencesNoorossana, R., Saghaei, A. & Amiri, A. (2011), Statistical Analysis of Profile Monitoring, Wiley Series in Probability and Statistics, Jhon Wiley & Sons, Inc.spa
dc.relation.referencesNoorossana, R. & Vaghe_, S. (2006), “E_ect of autocorrelation on performance of the MCUSUM control chart”, Quality Reliability Engeenering International 22, 191-197.spa
dc.relation.referencesOsei-Aning, R., Abbasi, S. A. & Riaz, M. (2017a), “Mixed EWMA-CUSUM and mixed CUSUM-EWMA modified control charts for monitoring first order autoregressive processes”, Quality Technology and Quantitative Management 14(4), 429-453. URL: http://dx.doi.org/10.1080/16843703.2017.1304038spa
dc.relation.referencesOsei-Aning, R., Abbasi, S. A. & Riaz, M. (2017b), “Monitoring of serially correlated processes using residual control charts”, Scientia Iranica 24(3), 1603-1614.spa
dc.relation.referencesPan, J.-N. & Chen, S.-T. (2008), “Monitoring long-memory air quality data using ARFIMA model”, Environmetrics 19(2), 209-219.spa
dc.relation.referencesPan, X. & Jarret, J. (2007), “Using vector autoregressive residuals to monitor multivariate processes in the presence of serial correlation”, International journal of production economics 106, 204-216.spa
dc.relation.referencesPaynabar, K. & Jin, J. (2011), “Characterization of non-linear profiles variations using mixed-effect models and wavelets”, IIE Transactions (Institute of Industrial Engineers) 43(4), 275-290.spa
dc.relation.referencesPilavakis, D., Paparoditis, E. & Sapatinas, T. (2019), “Moving block and tapered block bootstrap for functional time series with an application to the Ksample mean problem”, Bernoulli 25(4B), 3496-3526. URL: https://doi.org/10.3150/18-BEJ1099spa
dc.relation.referencesPsarakis, S. & Papaleonida, G. E. A. (2007), “SPC Procedures for Monitoring Autocorrelated Processes”, Quality Technology & Quantitative Management 4(4), 501-540.spa
dc.relation.referencesQiu, P. (2013), Introduction to statistical process control, CRC Press.spa
dc.relation.referencesQiu, P., Zou, C. & Wang, Z. (2010), “Nonparametric profile monitoring by mixed effects modeling”, Technometrics 52(3), 265-277.spa
dc.relation.referencesRamsay, J. & Silverman, B. W. (2005), Functional Data Analysis, Springer Series in Statistics, Springer.spa
dc.relation.referencesReynolds, M. R., Arnold, J. C. & Baik, J. W. (1996), “Variable sampling interval X charts in the presence of correlation”, Journal of Quality Technology 28(1), 12-30. URL: https://doi.org/10.1080/00224065.1996.11979633spa
dc.relation.referencesRoberts, S. W. (1959), “Control chart tests based on geometric moving averages”, Techno- metrics 1(3), 239-250. URL: https://www.tandfonline.com/doi/abs/10.1080/00401706.1959.10489860spa
dc.relation.referencesRyan, T. P. (2011), Statistical Methods for Quality Improvement: Third Edition, 3 edn, John Wiley & Sons.spa
dc.relation.referencesSheu, S. H., Ouyoung, C. W. & Hsu, T. S. (2013), “Phase II statistical process control for functional data”, Journal of Statistical Computation and Simulation 83(11), 2144-2159.spa
dc.relation.referencesShiau, J.-J. H. & Ya-Chen, H. (2005), “Robustness of the EWMA Control Chart to Nonnormality for Autocorrelated Processes”, Quality Technology & Quantitative Management 2(2), 125-146. URL: https://doi.org/10.1080/16843703.2005.11673089spa
dc.relation.referencesShongwe, S. & Malela-Majika, J. (2019), “Shewhart-type monitoring schemes with suplementary w-of-w run-rules to monitor the mean of autocorrelated samples”, Communications in Statistics - Simulation and Computation pp. 1-30.spa
dc.relation.referencesSiddiqui, Z. & Abdel-Salam, A. S. G. (2019), “A semiparametric profile monitoring via residuals”, Quality and Reliability Engineering International 35(4), 959-977.spa
dc.relation.referencesSteiner, S., Jensen, W. A., Grimshaw, S. D. & Espen, B. (2016), “Nonlinear profile monitoring for oven-temperature data”, Journal of Quality Technology 48(1), 84-97.spa
dc.relation.referencesTang, L. C. & Cheong, W. T. (2006), “A control scheme for high-yield correlated production under group inspection”, Journal of Quality Technology 38(1), 45-55. URL: https://doi.org/10.1080/00224065.2006.11918583spa
dc.relation.referencesVanbrackle, L. N. & Reynolds, M. R. J. (1997), “EWMA and CUSUM control charts in the presence of correlation”, Communications in Statistics - Simulation and Computation 26(3), 979-1008. URL: https://doi.org/10.1080/03610919708813421spa
dc.relation.referencesWalker, E. & Wright, S. P. (2002), “Comparing curves using additive models”, Journal of Quality Technology 34(1), 118-129.spa
dc.relation.referencesWang, H., Kim, S. H., Huo, X., Hur, Y. & Wilson, J. R. (2015), “Monitoring nonlinear profiles adaptively with a wavelet-based distribution-free CUSUM chart”, International Journal of Production Research 53(15), 4648-4667. URL: http://dx.doi.org/10.1080/00207543.2015.1029085spa
dc.relation.referencesWardell, D., Moskowitz, H. & Plante, R. (1994), “Control charts in the presence of data autocorrelation”, Management Science 38(8), 1084-1105.spa
dc.relation.referencesWilliams, J. D. (2011), Parametric Nonlinear Profiles, in R. Noorossana, A. Saghaei & A. Amiri, eds, “Statistical Analysis of Profile Monitoring”, Wiley Series in Probability and Statistics, Jhon Wiley & Sons, Inc, chapter 5, pp. 129-156.spa
dc.relation.referencesWoodall, W. (2017), “Bridging the gap between theory and practice in basic statistical process monitoring”, Quality Engineering 29(1), 2-15.spa
dc.relation.referencesWoodall, W. H. (2007), “Current research on profile monitoring”, Producao 17(3), 420-425.spa
dc.relation.referencesWoodall, W. H. & Montgomery, D. C. (1999), “Research Issues and Ideas in Statistical Process Control”, Journal of Quality Technology 31(4), 376-386. URL: https://doi.org/10.1080/00224065.1999.11979944spa
dc.relation.referencesZhang, J., Ren, H., Yao, R., Zou, C. & Wang, Z. (2015), “Phase I analysis of multivariate profiles based on regression adjustment”, Computers and Industrial Engineering 85, 132- 144. URL: http://www.sciencedirect.com/science/article/pii/S0360835215001047spa
dc.relation.referencesZhang, N. (1998), “A statistical control chart for stationary process data”, Technometrics 40(1), 24-39.spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseReconocimiento 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/spa
dc.subject.ddc510 - Matemáticasspa
dc.subject.lembAnálisis multivariantespa
dc.subject.lembMultivariate analysiseng
dc.subject.proposalFunctional time serieseng
dc.subject.proposalCartas de Controlspa
dc.subject.proposalDatos Funcionalesspa
dc.subject.proposalPerfiles no Linealesspa
dc.subject.proposalSeries de Tiempo Funcionalesspa
dc.subject.proposalControl chartseng
dc.subject.proposalNonlinear profileseng
dc.subject.proposalFunctional dataeng
dc.titleMonitoreo de perfiles no lineales con dependencia temporal en fase II desde un enfoque del análisis de datos funcionalesspa
dc.title.translatedPhase II monitoring of nonlinear profiles with temporal dependece using a functional data analysis approacheng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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