Advances in Statistical Monitoring of Complex Multivariate Processes : With Applications in Industrial Process Control.
Title:
Advances in Statistical Monitoring of Complex Multivariate Processes : With Applications in Industrial Process Control.
Author:
Kruger, Uwe.
ISBN:
9781118381267
Personal Author:
Edition:
1st ed.
Physical Description:
1 online resource (472 pages)
Series:
Statistics in Practice ; v.135
Statistics in Practice
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 variables -- 1.2.3 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 estimation -- 2.3.3 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 reconstruction -- 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 -- 6.1.1 Revisiting the eigendecomposition of Sz0z0 -- 6.1.2 Two illustrative examples -- 6.1.3 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 -- 6.2.6 Application to a distillation process-revisited -- 6.3 Robust model estimation -- 6.3.1 Robust parameter estimation -- 6.3.2 Trimming approaches -- 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 -- 7.3.1 Adapting the data correlation matrix -- 7.3.2 Adapting the eigendecomposition.
7.3.3 Computational analysis of the adaptation procedure -- 7.3.4 Adaptation of control limits -- 7.3.5 Process monitoring using an application delay -- 7.3.6 Minimum window length -- 7.4 A simulation example -- 7.4.1 Data generation -- 7.4.2 Application of PCA -- 7.4.3 Utilizing MWPCA based on an application delay -- 7.5 Application to a Fluid Catalytic Cracking Unit -- 7.5.1 Process description -- 7.5.2 Data generation -- 7.5.3 Pre-analysis of simulated data -- 7.5.4 Application of PCA -- 7.5.5 Application of MWPCA -- 7.6 Application to a furnace process -- 7.6.1 Process description -- 7.6.2 Description of sensor bias -- 7.6.3 Application of PCA -- 7.6.4 Utilizing MWPCA based on an application delay -- 7.7 Adaptive partial least squares -- 7.7.1 Recursive adaptation of Sx0x0 and Sx0y0 -- 7.7.2 Moving window adaptation of Sx0x0 and Sx0y0 -- 7.7.3 Adapting the number of source signals -- 7.7.4 Adaptation of the PLS model -- 7.8 Tutorial Session -- Chapter 8 Monitoring changes in covariance structure -- 8.1 Problem analysis -- 8.1.1 First intuitive example -- 8.1.2 Generic statistical analysis -- 8.1.3 Second intuitive example -- 8.2 Preliminary discussion of related techniques -- 8.3 Definition of primary and improved residuals -- 8.3.1 Primary residuals for eigenvectors -- 8.3.2 Primary residuals for eigenvalues -- 8.3.3 Comparing both types of primary residuals -- 8.3.4 Statistical properties of primary residuals -- 8.3.5 Improved residuals for eigenvalues -- 8.4 Revisiting the simulation examples of Section 8.1 -- 8.4.1 First simulation example -- 8.4.2 Second simulation example -- 8.5 Fault isolation and identification -- 8.5.1 Diagnosis of step-type fault conditions -- 8.5.2 Diagnosis of general deterministic fault conditions -- 8.5.3 A simulation example.
8.6 Application study of a gearbox system -- 8.6.1 Process description -- 8.6.2 Fault description -- 8.6.3 Identification of a monitoring model -- 8.6.4 Detecting a fault condition -- 8.7 Analysis of primary and improved residuals -- 8.7.1 Central limit theorem -- 8.7.2 Further statistical properties of primary residuals -- 8.7.3 Sensitivity of statistics based on 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.
Abstract:
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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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