Advances in Statistical Monitoring of Complex Multivariate Processes : With Applications in Industrial Process Control.
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 appl...
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| Format: | Electronic eBook |
| Language: | English |
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Hoboken :
Wiley,
2012.
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| Series: | Statistics in practice.
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| Online Access: |
Full text (Emmanuel users only) |
Table of Contents:
- Statistical Monitoring of Complex Multivariate Processes; Contents; 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; 1.1.1 Roots and evolution of statistical process control; 1.1.2 Principles of statistical process control; 1.1.3 Hypothesis testing, Type I and II errors; 1.2 Why multivariate statistical process control; 1.2.1 Statistically uncorrelated variables.
- 1.2.2 Perfectly correlated variables1.2.3 Highly correlated variables; 1.2.4 Type I and II errors and dimension reduction; 1.3 Tutorial session; Chapter 2 Multivariate data modeling methods; 2.1 Principal component analysis; 2.1.1 Assumptions for underlying data structure; 2.1.2 Geometric analysis of data structure; 2.1.3 A simulation example; 2.2 Partial least squares; 2.2.1 Assumptions for underlying data structure; 2.2.2 Deflation procedure for estimating data models; 2.2.3 A simulation example; 2.3 Maximum redundancy partial least squares; 2.3.1 Assumptions for underlying data structure.
- 2.3.2 Source signal estimation2.3.3 Geometric analysis of data structure; 2.3.4 A simulation example; 2.4 Estimating the number of source signals; 2.4.1 Stopping rules for PCA models; 2.4.2 Stopping rules for PLS models; 2.5 Tutorial Session; Chapter 3 Process monitoring charts; 3.1 Fault detection; 3.1.1 Scatter diagrams; 3.1.2 Non-negative quadratic monitoring statistics; 3.2 Fault isolation and identification; 3.2.1 Contribution charts; 3.2.2 Residual-based tests; 3.2.3 Variable reconstruction; 3.3 Geometry of variable projections; 3.3.1 Linear dependency of projection residuals.
- 3.3.2 Geometric analysis of variable reconstruction3.4 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; 6.1.1 Revisiting the eigendecomposition of Sz0z0.
- 6.1.2 Two illustrative examples6.1.3 Maximum likelihood PCA for known Sgg; 6.1.4 Maximum likelihood PCA for unknown Sgg; 6.1.5 A simulation example; 6.1.6 A stopping rule for maximum likelihood PCA models; 6.1.7 Properties of model and residual subspace estimates; 6.1.8 Application to a chemical reaction process-revisited; 6.2 Accuracy of estimating PLS models; 6.2.1 Bias and variance of parameter estimation; 6.2.2 Comparing accuracy of PLS and OLS regression models; 6.2.3 Impact of error-in-variables structure upon PLS models; 6.2.4 Error-in-variable estimate for known See.
- 6.2.5 Error-in-variable estimate for unknown See.