| Titre : | Bayesian process monitoring, control and optimization | | Type de document : | texte imprimé | | Auteurs : | Bianca M. Colosimo, Auteur ; Enrique Del Castillo, Auteur | | Editeur : | Boca Raton : Chapman & Hall/CRC | | Année de publication : | 2007 | | Importance : | 336 p. | | Présentation : | couv. ill.,ill. | | Format : | 24,1 cm. | | ISBN/ISSN/EAN : | 978-1-584-88544-3 | | Langues : | Anglais (eng) | | Catégories : | AUTOMATISME
| | Index. décimale : | 25-07 Théorie de la commande: commandes des processus | | Résumé : | 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 development, this reference adopts Bayesian approaches for actual industrial practices. Divided into four parts, it begins with an introduction that discusses inferential problems and presents modern methods in Bayesian computation. The next part explains statistical process control (SPC) and examines both univariate and multivariate process monitoring techniques. Subsequent chapters present Bayesian approaches that can be used for time series data analysis and process control. The contributors include material on the Kalman filter, radar detection, and discrete part manufacturing. The last part focuses on process optimization and illustrates the application of Bayesian regression to sequential optimization, the use of Bayesian techniques for the analysis of saturated designs, and the function of predictive distributions for optimization.
Written by international contributors from academia and industry, Bayesian Process Monitoring, Control and Optimization provides up-to-date applications of Bayesian processes for industrial, mechanical, electrical, and quality engineers as well as applied statisticians. | | Note de contenu : | Contents:
Part I INTRODUCTION TO BAYESIAN INFERENCE
1 An Introduction to Bayesian Inference in Process Monitoring, Control, and Optimization
2 Modern Numerical Methods in Bayesian Computation
Part II PROCESS MONITORING
3 A Bayesian Approach to Statistical Process Control
4 Empirical Bayes Process Monitoring Techniques
5 A Bayesian Approach to Monitoring the Mean of a Multivariate Normal Process
6 Two-Sided Bayesian Control Charts for Short Production Runs
7 Bayes' Rule of Information and Monitoring in Manufacturing Integrated Circuits
Part III PROCESS CONTROL AND TIME SERIES ANALYSIS
8 A Bayesian Approach to Signal Analysis of Pulse Trains
9 Bayesian Approaches to Process Monitoring and Process Adjustment
Part IV PROCESS OPTIMIZATION AND DESIGNED EXPERIMENTS
10 A Review of Bayesian Reliability Approaches to Multiple Response Surface Optimization
11 An Application of Bayesian Statistics to Sequential Empirical Optimization
12 Bayesian Estimation from Saturated Factorial Designs
Index |
Bayesian process monitoring, control and optimization [texte imprimé] / Bianca M. Colosimo, Auteur ; Enrique Del Castillo, Auteur . - Boca Raton : Chapman & Hall/CRC, 2007 . - 336 p. : couv. ill.,ill. ; 24,1 cm. ISBN : 978-1-584-88544-3 Langues : Anglais ( eng) | Catégories : | AUTOMATISME
| | Index. décimale : | 25-07 Théorie de la commande: commandes des processus | | Résumé : | 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 development, this reference adopts Bayesian approaches for actual industrial practices. Divided into four parts, it begins with an introduction that discusses inferential problems and presents modern methods in Bayesian computation. The next part explains statistical process control (SPC) and examines both univariate and multivariate process monitoring techniques. Subsequent chapters present Bayesian approaches that can be used for time series data analysis and process control. The contributors include material on the Kalman filter, radar detection, and discrete part manufacturing. The last part focuses on process optimization and illustrates the application of Bayesian regression to sequential optimization, the use of Bayesian techniques for the analysis of saturated designs, and the function of predictive distributions for optimization.
Written by international contributors from academia and industry, Bayesian Process Monitoring, Control and Optimization provides up-to-date applications of Bayesian processes for industrial, mechanical, electrical, and quality engineers as well as applied statisticians. | | Note de contenu : | Contents:
Part I INTRODUCTION TO BAYESIAN INFERENCE
1 An Introduction to Bayesian Inference in Process Monitoring, Control, and Optimization
2 Modern Numerical Methods in Bayesian Computation
Part II PROCESS MONITORING
3 A Bayesian Approach to Statistical Process Control
4 Empirical Bayes Process Monitoring Techniques
5 A Bayesian Approach to Monitoring the Mean of a Multivariate Normal Process
6 Two-Sided Bayesian Control Charts for Short Production Runs
7 Bayes' Rule of Information and Monitoring in Manufacturing Integrated Circuits
Part III PROCESS CONTROL AND TIME SERIES ANALYSIS
8 A Bayesian Approach to Signal Analysis of Pulse Trains
9 Bayesian Approaches to Process Monitoring and Process Adjustment
Part IV PROCESS OPTIMIZATION AND DESIGNED EXPERIMENTS
10 A Review of Bayesian Reliability Approaches to Multiple Response Surface Optimization
11 An Application of Bayesian Statistics to Sequential Empirical Optimization
12 Bayesian Estimation from Saturated Factorial Designs
Index |
|  |
Affiner la recherche Interroger des sources externesBayesian process monitoring, control and optimization / Bianca M. Colosimo
