07649nam 2200637 a 450 991013909320332120200520144314.01-283-54977-897866138622280-470-51725-50-470-51724-71-118-38126-2(CKB)2560000000089733(EBL)995876(SSID)ssj0000700904(PQKBManifestationID)11427609(PQKBTitleCode)TC0000700904(PQKBWorkID)10673063(PQKB)11509648(Au-PeEL)EBL995876(CaPaEBR)ebr10593226(CaONFJC)MIL386222(CaSebORM)9780470517246(MiAaPQ)EBC995876(OCoLC)794922820(EXLCZ)99256000000008973320120423d2012 uy 0engur|n|---|||||txtccrStatistical monitoring of complex multivariate processes[electronic resource] with applications in industrial process control /Uwe Kruger and Lei Xie1st editionChichester [England] ;Hoboken, N.J. Wiley20121 online resource (472 p.)Statistics in practiceDescription based upon print version of record.0-470-02819-X Includes bibliographical references and index.Machine generated contents note: Preface Introduction I Fundamentals of Multivariate Statistical Process Control 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 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 3 Process Monitoring Charts 3.1 Fault Detection 3.1.1 Scatter Diagrams 3.1.2 Nonnegative 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 II Application Studies 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 5 Application to a Distillation Process 5.1 Process Description 5.2 Identification of a Monitoring Model 5.3 Diagnosis of a Fault Condition III Advances in Multivariate Statistical Process Control 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 7 Monitoring Multivariate Time-Varying Processes 7.1 Problem Analysis 7.2 Recursive Principal Component Analysis 7.3 MovingWindow 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 MinimumWindow 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 MovingWindow Adaptation of Sv0v0 and Sv0y0 7.7.3 Adapting The Number of Source Signals 7.7.4 Adaptation of the PLS Model 7.8 Tutorial Session 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 in 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 to 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 IV Description of Modeling Methods 9 Principal Component Analysis 9.1 The Core Algorithm 9.2 Summary of the PCA Algorithm 9.3 Properties of a PCA Model 10 Partial Least Squares 10.1 Preliminaries 10.2 The Core Algorithm 10.3 Summary of the PLS Algorithm10.4 Properties of PLS 10.5 Properties of Maximum Redundancy PLS References Index."The book summarises recent advances in statistical-based process monitoring of complex multivariate process systems"--Provided by publisher.Statistics in PracticeMultivariate analysisMultivariate analysis.519.5/35MAT029020bisacshKrüger UweDr.427599Xie Lei894403MiAaPQMiAaPQMiAaPQBOOK9910139093203321Statistical monitoring of complex multivariate processes1998125UNINA01010nam a22002651i 450099100243466970753620030617083125.0030925s1979 pl |||||||||||||||||eng b12288305-39ule_instARCHE-033587ExLBiblioteca InterfacoltàitaA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l.531.38Hueckel, Tomasz24523Some problems in elastoplasticity /Tomasz Hueckel, Jan A. KonigWroclaw [etc.] :Zaklad Narodowy Imienia Ossolinskich,197927 p. ;25 cmConferenze ;74Deformazioni plasticheStudiKonig, Jan A..b1228830502-04-1408-10-03991002434669707536LE002 Ann. 420/07412002000142788le002-E0.00-l- 00000.i1268180508-10-03Some problems in elastoplasticity164329UNISALENTOle00208-10-03ma -engpl 0103899oam 2200649I 450 991080019320332120230803194855.00-429-07356-91-62870-756-91-4665-9249-410.1201/b15160(CKB)2670000000395036(EBL)1316409(OCoLC)854520660(SSID)ssj0000876682(PQKBManifestationID)11465782(PQKBTitleCode)TC0000876682(PQKBWorkID)10923357(PQKB)11144997(OCoLC)857078778(MiAaPQ)EBC1316409(OCoLC)852898449(EXLCZ)99267000000039503620180331d2014 uy 0engur|n|---|||||txtccrConcrete and sustainability /Per Jahren, Tongbo SuiBoca Raton :Taylor and Francis, CRC Press,2014.1 online resource (437 p.)A Spon Press book.1-138-07350-4 1-4665-9250-8 Includes bibliographical references and index.Front Cover; Contents; Foreword; Preface; Acknowledgements; The authors; About this book; Chapter 1 - Introduction; Chapter 2 - Environmental issues; Chapter 3 - Emissions and absorptions; Chapter 4 - Recycling; Chapter 5 - The environmental challenges-other items; Chapter 6 - New possibilities and challenges; Chapter 7 - The future; References; Back CoverConcrete is the second most common commodity in the world, after water, and by far the most common building material. The industry has a great deal of responsibility for sustainable development. This book demonstrates the importance of sustainable thinking, examines the range of challenges facing the concrete engineer, and outlines how they can be addressed. It balances account resource availability, technical viability, economical feasibility, environmental sustainability and social responsibility. It presents a holistic view of the environmental challenges and conveys the complexity of the topic, while giving examples of good practice in various aspects from around the world--Provided by publisher.In view of the development of world concrete and construction, we see an evolution of the focus in the direction of: Safety Durability Serviceability/Functionality Sustainability It is important in this context to learn at least two things: - All the focuses in the evolution process are closely linked to each other and function upon need instead of occurring and existing independently or replacing one by another. - The latest developed focus - Sustainability is not only evolved from the previous focuses but works as a function of them as well. We therefore believe that sustainability is not only an environmental performance, it is indeed a holistic thinking/approach that can be considered as the function of safety, durability, functionality and economical feasibility, environmental compatibility and social responsibility. The level/magnitude of each focus to sustainability varies depending on the specific requirement of the target and local boundary conditions--Provided by publisher.Concrete constructionConcreteEnvironmental aspectsSustainable constructionConcrete construction.ConcreteEnvironmental aspects.Sustainable construction.624.1/8340286624.18340286TEC005000TEC063000bisacshJahren Per.1061007Sui Tongbo1587222MiAaPQMiAaPQMiAaPQBOOK9910800193203321Concrete and sustainability3874716UNINA