Uwe Kruger, Lei Xie

Statistical Monitoring of Complex Multivatiate Processes (eBook, ePUB)

With Applications in Industrial Process Control

• Format: ePub

Produktbeschreibung

The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike. Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering. The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applications. In contrast, competitive model, signal or knowledge based techniques showed their potential only whenever cost-benefit economics have justified the required effort in developing applications. Statistical Monitoring of Complex Multivariate Processes presents recent advances in statistics based process monitoring, explaining how these processes can now be used in areas such as mechanical and manufacturing engineering for example, in addition to the traditional chemical industry. This book: * Contains a detailed theoretical background of the component technology. * Brings together a large body of work to address the field's drawbacks, and develops methods for their improvement. * Details cross-disciplinary utilization, exemplified by examples in chemical, mechanical and manufacturing engineering. * Presents real life industrial applications, outlining deficiencies in the methodology and how to address them. * Includes numerous examples, tutorial questions and homework assignments in the form of individual and team-based projects, to enhance the learning experience. * Features a supplementary website including Matlab algorithms and data sets. This book provides a timely reference text to the rapidly evolving area of multivariate statistical analysis for academics, advanced level students, and practitioners alike.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 472
• Erscheinungstermin: 22. August 2012
• Englisch
• ISBN-13: 9780470517246
• Artikelnr.: 37296419

Autorenporträt

Uwe Kruger, The Petroleum Institute, Abu Dhabi, United Arab Emirates Lei Xie, Institute of Cyber-Systems & Control, Zhejiang University, China

Inhaltsangabe

Preface xiii Acknowledgements xvii Abbreviations xix Symbols xxi
Nomenclature xxiii Introduction xxv PART I FUNDAMENTALS OF MULTIVARIATE
STATISTICAL PROCESS CONTROL 1 1 Motivation for multivariate statistical
process control 3 1.1 Summary of statistical process control 3 1.1.1 Roots
and evolution of statistical process control 4 1.1.2 Principles of
statistical process control 5 1.1.3 Hypothesis testing, Type I and II
errors 12 1.2 Why multivariate statistical process control 15 1.2.1
Statistically uncorrelated variables 16 1.2.2 Perfectly correlated
variables 17 1.2.3 Highly correlated variables 19 1.2.4 Type I and II
errors and dimension reduction 24 1.3 Tutorial session 26 2 Multivariate
data modeling methods 28 2.1 Principal component analysis 30 2.1.1
Assumptions for underlying data structure 30 2.1.2 Geometric analysis of
data structure 33 2.1.3 A simulation example 34 2.2 Partial least squares
38 2.2.1 Assumptions for underlying data structure 39 2.2.2 Deflation
procedure for estimating data models 41 2.2.3 A simulation example 43 2.3
Maximum redundancy partial least squares 49 2.3.1 Assumptions for
underlying data structure 49 2.3.2 Source signal estimation 50 2.3.3
Geometric analysis of data structure 52 2.3.4 A simulation example 58 2.4
Estimating the number of source signals 65 2.4.1 Stopping rules for PCA
models 65 2.4.2 Stopping rules for PLS models 76 2.5 Tutorial Session 79 3
Process monitoring charts 81 3.1 Fault detection 83 3.1.1 Scatter diagrams
84 3.1.2 Non-negative quadratic monitoring statistics 85 3.2 Fault
isolation and identification 93 3.2.1 Contribution charts 95 3.2.2
Residual-based tests 98 3.2.3 Variable reconstruction 100 3.3 Geometry of
variable projections 111 3.3.1 Linear dependency of projection residuals
111 3.3.2 Geometric analysis of variable reconstruction 112 3.4 Tutorial
session 119 PART II APPLICATION STUDIES 121 4 Application to a chemical
reaction process 123 4.1 Process description 123 4.2 Identification of a
monitoring model 124 4.3 Diagnosis of a fault condition 133 5 Application
to a distillation process 141 5.1 Process description 141 5.2
Identification of a monitoring model 144 5.3 Diagnosis of a fault condition
153 PART III ADVANCES IN MULTIVARIATE STATISTICAL PROCESS CONTROL 165 6
Further modeling issues 167 6.1 Accuracy of estimating PCA models 168 6.1.1
Revisiting the eigendecomposition of Sz0z0 168 6.1.2 Two illustrative
examples 171 6.1.3 Maximum likelihood PCA for known Sgg 172 6.1.4 Maximum
likelihood PCA for unknown Sgg 177 6.1.5 A simulation example 182 6.1.6 A
stopping rule for maximum likelihood PCA models 187 6.1.7 Properties of
model and residual subspace estimates 189 6.1.8 Application to a chemical
reaction process - revisited 194 6.2 Accuracy of estimating PLS models 202
6.2.1 Bias and variance of parameter estimation 203 6.2.2 Comparing
accuracy of PLS and OLS regression models 205 6.2.3 Impact of
error-in-variables structure upon PLS models 212 6.2.4 Error-in-variable
estimate for known See 218 6.2.5 Error-in-variable estimate for unknown See
219 6.2.6 Application to a distillation process - revisited 223 6.3 Robust
model estimation 226 6.3.1 Robust parameter estimation 228 6.3.2 Trimming
approaches 231 6.4 Small sample sets 232 6.5 Tutorial session 237 7
Monitoring multivariate time-varying processes 240 7.1 Problem analysis 241
7.2 Recursive principal component analysis 242 7.3 Moving window principal
component analysis 244 7.3.1 Adapting the data correlation matrix 244 7.3.2
Adapting the eigendecomposition 247 7.3.3 Computational analysis of the
adaptation procedure 251 7.3.4 Adaptation of control limits 252 7.3.5
Process monitoring using an application delay 253 7.3.6 Minimum window
length 254 7.4 A simulation example 257 7.4.1 Data generation 257 7.4.2
Application of PCA 258 7.4.3 Utilizing MWPCA based on an application delay
261 7.5 Application to a Fluid Catalytic Cracking Unit 265 7.5.1 Process
description 266 7.5.2 Data generation 268 7.5.3 Pre-analysis of simulated
data 272 7.5.4 Application of PCA 273 7.5.5 Application of MWPCA 275 7.6
Application to a furnace process 278 7.6.1 Process description 278 7.6.2
Description of sensor bias 279 7.6.3 Application of PCA 280 7.6.4 Utilizing
MWPCA based on an application delay 282 7.7 Adaptive partial least squares
286 7.7.1 Recursive adaptation of 7.7.2 Moving window adaptation of 7.7.3
Adapting the number of source signals 287 7.7.4 Adaptation of the PLS model
290 7.8 Tutorial Session 292 Sx0x0 and Sx0y0 287Sx0x0 and Sx0y0 287 8
Monitoring changes in covariance structure 293 8.1 Problem analysis 294
8.1.1 First intuitive example 294 8.1.2 Generic statistical analysis 297
8.1.3 Second intuitive example 299 8.2 Preliminary discussion of related
techniques 304 8.3 Definition of primary and improved residuals 305 8.3.1
Primary residuals for eigenvectors 306 8.3.2 Primary residuals for
eigenvalues 307 8.3.3 Comparing both types of primary residuals 307 8.3.4
Statistical properties of primary residuals 312 8.3.5 Improved residuals
for eigenvalues 315 8.4 Revisiting the simulation examples of Section 8.1
317 8.4.1 First simulation example 318 8.4.2 Second simulation example 321
8.5 Fault isolation and identification 324 8.5.1 Diagnosis of step-type
fault conditions 325 8.5.2 Diagnosis of general deterministic fault
conditions 328 8.5.3 A simulation example 328 8.6 Application study of a
gearbox system 331 8.6.1 Process description 332 8.6.2 Fault description
332 8.6.3 Identification of a monitoring model 334 8.6.4 Detecting a fault
condition 338 8.7 Analysis of primary and improved residuals 341 8.7.1
Central limit theorem 341 8.7.2 Further statistical properties of primary
residuals 344 8.7.3 Sensitivity of statistics based on improved residuals
349 8.8 Tutorial session 353 PART IV DESCRIPTION OF MODELING METHODS 355 9
Principal component analysis 357 9.1 The core algorithm 357 9.2 Summary of
the PCA algorithm 362 9.3 Properties of a PCA model 363 10 Partial least
squares 375 10.1 Preliminaries 375 10.2 The core algorithm 377 10.3 Summary
of the PLS algorithm 380 10.4 Properties of PLS 381 10.5 Properties of
maximum redundancy PLS 396 References 410 Index 427