Home Page Icon
Home Page
Table of Contents for
Part II: Application Studies
Close
Part II: Application Studies
by Lei Xie, Uwe Kruger
Statistical Monitoring of Complex Multivariate Processes: With Applications in Industrial Process Control
Cover
Series Page
Title Page
Copyright
Dedication
Preface
Acknowledgements
Abbreviations
Symbols
Nomenclature
Introduction
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
1.3 Tutorial session
Chapter 2: Multivariate data modeling methods
2.1 Principal component analysis
2.2 Partial least squares
2.3 Maximum redundancy partial least squares
2.4 Estimating the number of source signals
2.5 Tutorial Session
Chapter 3: Process monitoring charts
3.1 Fault detection
3.2 Fault isolation and identification
3.3 Geometry of variable projections
3.4 Tutorial session
Part II: Application Studies
Chapter 4: Application to a chemical reaction process
4.1 Process description
4.2 Identification of a monitoring model
4.3 Diagnosis of a fault condition
Chapter 5: Application to a distillation process
5.1 Process description
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
6.3 Robust model estimation
6.4 Small sample sets
6.5 Tutorial session
Chapter 7: Monitoring multivariate time-varying processes
7.1 Problem analysis
7.2 Recursive principal component analysis
7.3 Moving window principal component analysis
7.4 A simulation example
7.5 Application to a Fluid Catalytic Cracking Unit
7.6 Application to a furnace process
7.7 Adaptive partial least squares
7.8 Tutorial Session
Chapter 8: Monitoring changes in covariance structure
8.1 Problem analysis
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
8.8 Tutorial session
Part IV: Description of Modeling Methods
Chapter 9: Principal component analysis
9.1 The core algorithm
9.2 Summary of the PCA algorithm
9.3 Properties of a PCA model
Chapter 10: Partial least squares
10.1 Preliminaries
10.2 The core algorithm
10.3 Summary of the PLS algorithm
10.4 Properties of PLS
10.5 Properties of maximum redundancy PLS
References
Index
Statistics in Practice
Search in book...
Toggle Font Controls
Playlists
Add To
Create new playlist
Name your new playlist
Playlist description (optional)
Cancel
Create playlist
Sign In
Email address
Password
Forgot Password?
Create account
Login
or
Continue with Facebook
Continue with Google
Sign Up
Full Name
Email address
Confirm Email Address
Password
Login
Create account
or
Continue with Facebook
Continue with Google
Prev
Previous Chapter
Chapter 3: Process monitoring charts
Next
Next Chapter
Chapter 4: Application to a chemical reaction process
Part II
Application Studies
Add Highlight
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click
here
login for view all page.
Day Mode
Cloud Mode
Night Mode
Reset