Table of Contents
Part I: Fundamentals of Multivariate Statistical Process Control
Chapter 1: Motivation for multivariate statistical process control
1.1 Summary of statistical process control
1.2 Why multivariate statistical process control
Chapter 2: Multivariate data modeling methods
2.1 Principal component analysis
2.3 Maximum redundancy partial least squares
2.4 Estimating the number of source signals
Chapter 3: Process monitoring charts
3.2 Fault isolation and identification
3.3 Geometry of variable projections
Chapter 4: Application to a chemical reaction process
4.2 Identification of a monitoring model
4.3 Diagnosis of a fault condition
Chapter 5: Application to a distillation process
5.2 Identification of a monitoring model
5.3 Diagnosis of a fault condition
Part III: Advances in Multivariate Statistical Process Control
Chapter 6: Further modeling issues
6.1 Accuracy of estimating PCA models
6.2 Accuracy of estimating PLS models
Chapter 7: Monitoring multivariate time-varying processes
7.2 Recursive principal component analysis
7.3 Moving window principal component analysis
7.5 Application to a Fluid Catalytic Cracking Unit
7.6 Application to a furnace process
7.7 Adaptive partial least squares
Chapter 8: Monitoring changes in covariance structure
8.2 Preliminary discussion of related techniques
8.3 Definition of primary and improved residuals
8.4 Revisiting the simulation examples of Section 8.1
8.5 Fault isolation and identification
8.6 Application study of a gearbox system
8.7 Analysis of primary and improved residuals
Part IV: Description of Modeling Methods
Chapter 9: Principal component analysis
9.2 Summary of the PCA algorithm
Chapter 10: Partial least squares
10.3 Summary of the PLS algorithm