Download or read book Multivariate Bayesian Process Control written by Zhijian Yin and published by . This book was released on 2008 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate control charts are valuable tools for multivariate statistical process control (MSPC) used to monitor industrial processes and to detect abnormal process behavior. It has been shown in the literature that Bayesian control charts are optimal tools to control the process compared with the non-Bayesian charts. To use any control chart, three control chart parameters must be specified, namely the sample size, the sampling interval and the control limit. Traditionally, control chart design is based on its statistical performance. Recently, industrial practitioners and academic researchers have increasingly recognized the cost benefits obtained by applying the economically designed control charts to quality control, equipment condition monitoring, and maintenance decision-making. The primary objective of this research is to design multivariate Bayesian control charts (MVBCH) both for quality control and conditional-based maintenance (CBM) applications. Although considerable research has been done to develop MSPC tools under the assumption that the observations are independent, little attention has been given to the development of MSPC tools for monitoring multivariate autocorrelated processes. In this research, we compare the III performance of the squared predication error (SPE) chart using a vector autoregressive moving average with exogenous variables (VARMAX) model and a partial least squares (PLS) model for a multivariate autocorrelated process. The study shows that the use of SPE control charts based on the VARMAX model allows rapid detection of process disturbances while reducing false alarms.Next, the economic and economic-statistical design of a MVBCH for quality control considering the control limit policy proved to be optimal by Makis (2007) is developed. The computational results illustrate that the MVBCH performs considerably better than the MEWMA chart, especially for smaller mean shifts. Sensitivity analyses further explore the impact of the misspecified out-of-control mean on the actual average cost. Finally, design of a MVBCH for CBM applications is considered using the same control limit policy structure and including an observable failure state. Optimization models for the economic and economic statistical design of the MVBCH for a 3 state CBM model are developed and comparison results show that the MVBCH performs better than recently developed CBM Chi-square chart.

Download or read book Multivariate Bayesian Control Chart with Dual Sampling Scheme written by Farnoosh Naderkhani Zarrin Ghabaei and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, there has been a growing interest among industrial practitioners and researchers for applying economically designed control charts in quality control, condition monitoring, and condition-based maintenance (CBM). Control charts are powerful tools for process control which are used extensively to ensure the process stability over the production run. It has been proved that traditional control charts are not optimal and the control chart parameters should be determined based on the posterior probability that the process is out of control. Design and development of Bayesian control charts in both quality control and CBM applications are the main focuses of this thesis. Traditionally, in designing of a control chart, the observations are collected periodically. However, in many real applications, high sampling cost is associated with collecting observable data, therefore, it could be beneficial to monitor the process/system less frequently when it is in a healthier state and more frequently when a sample shows some indication of a change in the process/system state. The motivation of this thesis comes from such applications when collecting observations is costly, and observations carry partial information about the process or system state. To overcome the drawback of period monitoring, this thesis proposes an optimal control problem in Bayesian framework which uses a novel sampling strategy referred to as dual sampling scheme (DSS) and dual control limits. The system/process is monitored less frequently using a longer sampling interval when the posterior probability is below the warning limit. If the posterior probability exceeds the warning limit, switching to the shorter sampling interval occurs. If the posterior probability exceeds the control limit, the system/process is stopped and full inspection is performed. The proposed model is formulated in both semi-Markov decision process and renewal theory frameworks to obtain the optimal control chart parameters as well as the minimum long-run expected average cost per unit time. For the first time in quality control literature, an explicit formula is derived for computation of average time to signal for multivariate Bayesian control chart with DSS. In addition, the closed-form expressions are derived for system residual life and reliability as functions of the posterior probability statistics.

Download or read book Bayesian Process Monitoring Control and Optimization written by Bianca M. Colosimo and published by CRC Press. This book was released on 2006-11-10 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although there are many Bayesian statistical books that focus on biostatistics and economics, there are few that address the problems faced by engineers. Bayesian Process Monitoring, Control and Optimization resolves this need, showing you how to oversee, adjust, and optimize industrial processes. Bridging the gap between application and dev

Download or read book Multivariate Statistical Process Control written by Zhiqiang Ge and published by Springer Science & Business Media. This book was released on 2012-11-28 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality. Multivariate Statistical Process Control reviews the developments and improvements that have been made to MSPC over the last decade, and goes on to propose a series of new MSPC-based approaches for complex process monitoring. These new methods are demonstrated in several case studies from the chemical, biological, and semiconductor industrial areas. Control and process engineers, and academic researchers in the process monitoring, process control and fault detection and isolation (FDI) disciplines will be interested in this book. It can also be used to provide supplementary material and industrial insight for graduate and advanced undergraduate students, and graduate engineers. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Download or read book Applied Multivariate Analysis written by S. James Press and published by Courier Corporation. This book was released on 2012-09-05 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geared toward upper-level undergraduates and graduate students, this two-part treatment deals with the foundations of multivariate analysis as well as related models and applications. Starting with a look at practical elements of matrix theory, the text proceeds to discussions of continuous multivariate distributions, the normal distribution, and Bayesian inference; multivariate large sample distributions and approximations; the Wishart and other continuous multivariate distributions; and basic multivariate statistics in the normal distribution. The second half of the text moves from defining the basics to explaining models. Topics include regression and the analysis of variance; principal components; factor analysis and latent structure analysis; canonical correlations; stable portfolio analysis; classifications and discrimination models; control in the multivariate linear model; and structuring multivariate populations, with particular focus on multidimensional scaling and clustering. In addition to its value to professional statisticians, this volume may also prove helpful to teachers and researchers in those areas of behavioral and social sciences where multivariate statistics is heavily applied. This new edition features an appendix of answers to the exercises.

Download or read book Multi state Bayesian Process Control written by Jue Wang and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multivariate Statistical Process Control with Industrial Applications written by Robert L. Mason and published by Cambridge University Press. This book was released on 2002 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: A text for students or professionals involved with multivariate quality control who are knowledgeable about univariate statistical estimation and control procedures, and certain probability functions such as the normal and chi-square distributions. The coverage is limited to the Hotelling T2 statistic as being the most benefit to practitioners. The disk contains a demonstration version of statistical software compatible with recent Windows products. Annotation copyrighted by Book News Inc., Portland, OR.

Download or read book Univariate and Multivariate Statistical Process Control written by Murat Caner Testik and published by LAP Lambert Academic Publishing. This book was released on 2010-10 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: Univariate and multivariate quality control charts are important tools for process/product monitoring and improvement. This monograph offers the developments and analyses of univariate and multivariate control charts, which are based on a generalized likelihood ratio (GLR) approach. It is commonly assumed that a process fault may shift the mean of a monitored statistic persistently to an unknown but constant value. However, there are situations such that a mean change is not constant but time varying. Incorporating a priori knowledge of a fault signature, a univariate GLR control chart is investigated for monitoring fault signatures. The GLR methodology can also be used in developing multivariate process control charts. Here, the GLR methodology is used to unify the development of various multivariate extensions to the CUSUM control charts. In addition to the GLR control charts under a normal distribution model, another GLR control chart is also proposed for monitoring a non- homogenous Markovian queuing system.

Download or read book On Multivariate Process Control Charts written by V. Pemajayantha and published by . This book was released on 1998 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Multivariate Process Capability Indices written by Ashis Kumar Chakraborty and published by CRC Press. This book was released on 2021-01-06 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing a single-valued assessment of the performance of a process is often one of the greatest challenges for a quality professional. Process Capability Indices (PCIs) precisely do this job. For processes having a single measurable quality characteristic, there is an ample number of PCIs, defined in literature. The situation worsens for multivariate processes, i.e., where there is more than one correlated quality characteristic. Since in most situations quality professionals face multiple quality characteristics to be controlled through a process, Multivariate Process Capability Indices (MPCIs) become the order of the day. However, there is no book which addresses and explains different MPCIs and their properties. The literature of Multivariate Process Capability Indices (MPCIs) is not well organized, in the sense that a thorough and systematic discussion on the various MPCIs is hardly available in the literature. Handbook of Multivariate Process Capability Indices provides an extensive study of the MPCIs defined for various types of specification regions. This book is intended to help quality professionals to understand which MPCI should be used and in what situation. For researchers in this field, the book provides a thorough discussion about each of the MPCIs developed to date, along with their statistical and analytical properties. Also, real life examples are provided for almost all the MPCIs discussed in the book. This helps both the researchers and the quality professionals alike to have a better understanding of the MPCIs, which otherwise become difficult to understand, since there is more than one quality characteristic to be controlled at a time. Features: A complete guide for quality professionals on the usage of different MPCIs. A step by step discussion on multivariate process capability analysis, starting from a brief discussion on univariate indices. A single source for all kinds of MPCIs developed so far. Comprehensive analysis of the MPCIs, including analysis of real-life data. References provided at the end of each chapter encompass the entire literature available on the respective topic. Interpretation of the MPCIs and development of threshold values of many MPCIs are also included. This reference book is aimed at the post graduate students in Industrial Statistics. It will also serve researchers working in the field of Industrial Statistics, as well as practitioners requiring thorough guidance regarding selection of an appropriate MPCI suitable for the problem at hand.

Download or read book Multivariate Statistical Process Control written by Sijbrand Jacobus Wierda and published by . This book was released on 1994 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Bayesian Approach to Multivariate Quality Control written by Kamlesh Jain and published by . This book was released on 1993 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advances In Multivariate Statistical Methods written by Ashis Sengupta and published by World Scientific. This book was released on 2009-06-23 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains a collection of research articles on multivariate statistical methods, encompassing both theoretical advances and emerging applications in a variety of scientific disciplines. It serves as a tribute to Professor S N Roy, an eminent statistician who has made seminal contributions to the area of multivariate statistical methods, on his birth centenary. In the area of emerging applications, the topics include bioinformatics, categorical data and clinical trials, econometrics, longitudinal data analysis, microarray data analysis, sample surveys, statistical process control, etc.Researchers, professionals and advanced graduates will find the book an essential resource for modern developments in theory as well as for innovative and emerging important applications in the area of multivariate statistical methods.

Download or read book Multivariate Bayesian Statistics written by Daniel B. Rowe and published by CRC Press. This book was released on 2002-11-25 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients, but

Download or read book Introduction to Statistical Process Control written by Peihua Qiu and published by CRC Press. This book was released on 2013-10-14 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, Introduction to Statistical Process Control describes many recent SPC methods that improve upon

Download or read book Bayesian Data Analysis Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Download or read book Univariate and Multivariate Process Control Charts written by Gunabushanam Nedumaran and published by . This book was released on 1997 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: