{"product_id":"statistical-monitoring-of-complex-multivatiate-processes-9780470028193","title":"Statistical Monitoring of Complex Multivatiate","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eStatistical Monitoring of Complex Multivariate Processes summarizes recent advances in statistical-based process monitoring of complex multivariate process systems. The book includes a broad range of applications of multivariate statistical techniques into the area of mechanical, manufacturing, and power engineering.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcknowledgements xvii\u003c\/p\u003e \u003cp\u003eAbbreviations xix\u003c\/p\u003e \u003cp\u003eSymbols xxi\u003c\/p\u003e \u003cp\u003eNomenclature xxiii\u003c\/p\u003e \u003cp\u003eIntroduction xxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Fundamentals of Multivariate Statistical Process Control 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Motivation for multivariate statistical process control 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Summary of statistical process control 3\u003c\/p\u003e \u003cp\u003e1.1.1 Roots and evolution of statistical process control 4\u003c\/p\u003e \u003cp\u003e1.1.2 Principles of statistical process control 5\u003c\/p\u003e \u003cp\u003e1.1.3 Hypothesis testing, Type I and II errors 12\u003c\/p\u003e \u003cp\u003e1.2 Why multivariate statistical process control 15\u003c\/p\u003e \u003cp\u003e1.2.1 Statistically uncorrelated variables 16\u003c\/p\u003e \u003cp\u003e1.2.2 Perfectly correlated variables 17\u003c\/p\u003e \u003cp\u003e1.2.3 Highly correlated variables 19\u003c\/p\u003e \u003cp\u003e1.2.4 Type I and II errors and dimension reduction 24\u003c\/p\u003e \u003cp\u003e1.3 Tutorial session 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Multivariate data modeling methods 28\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Principal component analysis 30\u003c\/p\u003e \u003cp\u003e2.1.1 Assumptions for underlying data structure 30\u003c\/p\u003e \u003cp\u003e2.1.2 Geometric analysis of data structure 33\u003c\/p\u003e \u003cp\u003e2.1.3 A simulation example 34\u003c\/p\u003e \u003cp\u003e2.2 Partial least squares 38\u003c\/p\u003e \u003cp\u003e2.2.1 Assumptions for underlying data structure 39\u003c\/p\u003e \u003cp\u003e2.2.2 Deflation procedure for estimating data models 41\u003c\/p\u003e \u003cp\u003e2.2.3 A simulation example 43\u003c\/p\u003e \u003cp\u003e2.3 Maximum redundancy partial least squares 49\u003c\/p\u003e \u003cp\u003e2.3.1 Assumptions for underlying data structure 49\u003c\/p\u003e \u003cp\u003e2.3.2 Source signal estimation 50\u003c\/p\u003e \u003cp\u003e2.3.3 Geometric analysis of data structure 52\u003c\/p\u003e \u003cp\u003e2.3.4 A simulation example 58\u003c\/p\u003e \u003cp\u003e2.4 Estimating the number of source signals 65\u003c\/p\u003e \u003cp\u003e2.4.1 Stopping rules for PCA models 65\u003c\/p\u003e \u003cp\u003e2.4.2 Stopping rules for PLS models 76\u003c\/p\u003e \u003cp\u003e2.5 Tutorial Session 79\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Process monitoring charts 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Fault detection 83\u003c\/p\u003e \u003cp\u003e3.1.1 Scatter diagrams 84\u003c\/p\u003e \u003cp\u003e3.1.2 Non-negative quadratic monitoring statistics 85\u003c\/p\u003e \u003cp\u003e3.2 Fault isolation and identification 93\u003c\/p\u003e \u003cp\u003e3.2.1 Contribution charts 95\u003c\/p\u003e \u003cp\u003e3.2.2 Residual-based tests 98\u003c\/p\u003e \u003cp\u003e3.2.3 Variable reconstruction 100\u003c\/p\u003e \u003cp\u003e3.3 Geometry of variable projections 111\u003c\/p\u003e \u003cp\u003e3.3.1 Linear dependency of projection residuals 111\u003c\/p\u003e \u003cp\u003e3.3.2 Geometric analysis of variable reconstruction 112\u003c\/p\u003e \u003cp\u003e3.4 Tutorial session 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Application Studies 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Application to a chemical reaction process 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Process description 123\u003c\/p\u003e \u003cp\u003e4.2 Identification of a monitoring model 124\u003c\/p\u003e \u003cp\u003e4.3 Diagnosis of a fault condition 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Application to a distillation process 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Process description 141\u003c\/p\u003e \u003cp\u003e5.2 Identification of a monitoring model 144\u003c\/p\u003e \u003cp\u003e5.3 Diagnosis of a fault condition 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Advances in Multivariate Statistical Process Control 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Further modeling issues 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Accuracy of estimating PCA models 168\u003c\/p\u003e \u003cp\u003e6.1.1 Revisiting the eigendecomposition of S\u003csub\u003ez0z0\u003c\/sub\u003e 168\u003c\/p\u003e \u003cp\u003e6.1.2 Two illustrative examples 171\u003c\/p\u003e \u003cp\u003e6.1.3 Maximum likelihood PCA for known S\u003csub\u003egg\u003c\/sub\u003e 172\u003c\/p\u003e \u003cp\u003e6.1.4 Maximum likelihood PCA for unknown S\u003csub\u003egg\u003c\/sub\u003e 177\u003c\/p\u003e \u003cp\u003e6.1.5 A simulation example 182\u003c\/p\u003e \u003cp\u003e6.1.6 A stopping rule for maximum likelihood PCA models 187\u003c\/p\u003e \u003cp\u003e6.1.7 Properties of model and residual subspace estimates 189\u003c\/p\u003e \u003cp\u003e6.1.8 Application to a chemical reaction process – revisited 194\u003c\/p\u003e \u003cp\u003e6.2 Accuracy of estimating PLS models 202\u003c\/p\u003e \u003cp\u003e6.2.1 Bias and variance of parameter estimation 203\u003c\/p\u003e \u003cp\u003e6.2.2 Comparing accuracy of PLS and OLS regression models 205\u003c\/p\u003e \u003cp\u003e6.2.3 Impact of error-in-variables structure upon PLS models 212\u003c\/p\u003e \u003cp\u003e6.2.4 Error-in-variable estimate for known S\u003csub\u003eee\u003c\/sub\u003e 218\u003c\/p\u003e \u003cp\u003e6.2.5 Error-in-variable estimate for unknown S\u003csub\u003eee\u003c\/sub\u003e 219\u003c\/p\u003e \u003cp\u003e6.2.6 Application to a distillation process – revisited 223\u003c\/p\u003e \u003cp\u003e6.3 Robust model estimation 226\u003c\/p\u003e \u003cp\u003e6.3.1 Robust parameter estimation 228\u003c\/p\u003e \u003cp\u003e6.3.2 Trimming approaches 231\u003c\/p\u003e \u003cp\u003e6.4 Small sample sets 232\u003c\/p\u003e \u003cp\u003e6.5 Tutorial session 237\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Monitoring multivariate time-varying processes 240\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Problem analysis 241\u003c\/p\u003e \u003cp\u003e7.2 Recursive principal component analysis 242\u003c\/p\u003e \u003cp\u003e7.3 Moving window principal component analysis 244\u003c\/p\u003e \u003cp\u003e7.3.1 Adapting the data correlation matrix 244\u003c\/p\u003e \u003cp\u003e7.3.2 Adapting the eigendecomposition 247\u003c\/p\u003e \u003cp\u003e7.3.3 Computational analysis of the adaptation procedure 251\u003c\/p\u003e \u003cp\u003e7.3.4 Adaptation of control limits 252\u003c\/p\u003e \u003cp\u003e7.3.5 Process monitoring using an application delay 253\u003c\/p\u003e \u003cp\u003e7.3.6 Minimum window length 254\u003c\/p\u003e \u003cp\u003e7.4 A simulation example 257\u003c\/p\u003e \u003cp\u003e7.4.1 Data generation 257\u003c\/p\u003e \u003cp\u003e7.4.2 Application of PCA 258\u003c\/p\u003e \u003cp\u003e7.4.3 Utilizing MWPCA based on an application delay 261\u003c\/p\u003e \u003cp\u003e7.5 Application to a Fluid Catalytic Cracking Unit 265\u003c\/p\u003e \u003cp\u003e7.5.1 Process description 266\u003c\/p\u003e \u003cp\u003e7.5.2 Data generation 268\u003c\/p\u003e \u003cp\u003e7.5.3 Pre-analysis of simulated data 272\u003c\/p\u003e \u003cp\u003e7.5.4 Application of PCA 273\u003c\/p\u003e \u003cp\u003e7.5.5 Application of MWPCA 275\u003c\/p\u003e \u003cp\u003e7.6 Application to a furnace process 278\u003c\/p\u003e \u003cp\u003e7.6.1 Process description 278\u003c\/p\u003e \u003cp\u003e7.6.2 Description of sensor bias 279\u003c\/p\u003e \u003cp\u003e7.6.3 Application of PCA 280\u003c\/p\u003e \u003cp\u003e7.6.4 Utilizing MWPCA based on an application delay 282\u003c\/p\u003e \u003cp\u003e7.7 Adaptive partial least squares 286\u003c\/p\u003e \u003cp\u003e7.7.1 Recursive Adaptation of S\u003csub\u003eX0X0\u003c\/sub\u003e and S\u003csub\u003ex0y0 \u003c\/sub\u003e287\u003c\/p\u003e \u003cp\u003e7.7.2 Moving Window Adaptation of S\u003csub\u003eX0X0\u003c\/sub\u003e and S\u003csub\u003ex0y0\u003c\/sub\u003e 287\u003c\/p\u003e \u003cp\u003e7.7.3 Adapting the number of source signals 287\u003c\/p\u003e \u003cp\u003e7.7.4 Adaptation of the PLS model 290\u003c\/p\u003e \u003cp\u003e7.8 Tutorial Session 292\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Monitoring changes in covariance structure 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Problem analysis 294\u003c\/p\u003e \u003cp\u003e8.1.1 First intuitive example 294\u003c\/p\u003e \u003cp\u003e8.1.2 Generic statistical analysis 297\u003c\/p\u003e \u003cp\u003e8.1.3 Second intuitive example 299\u003c\/p\u003e \u003cp\u003e8.2 Preliminary discussion of related techniques 304\u003c\/p\u003e \u003cp\u003e8.3 Definition of primary and improved residuals 305\u003c\/p\u003e \u003cp\u003e8.3.1 Primary residuals for eigenvectors 306\u003c\/p\u003e \u003cp\u003e8.3.2 Primary residuals for eigenvalues 307\u003c\/p\u003e \u003cp\u003e8.3.3 Comparing both types of primary residuals 307\u003c\/p\u003e \u003cp\u003e8.3.4 Statistical properties of primary residuals 312\u003c\/p\u003e \u003cp\u003e8.3.5 Improved residuals for eigenvalues 315\u003c\/p\u003e \u003cp\u003e8.4 Revisiting the simulation examples of Section 8.1 317\u003c\/p\u003e \u003cp\u003e8.4.1 First simulation example 318\u003c\/p\u003e \u003cp\u003e8.4.2 Second simulation example 321\u003c\/p\u003e \u003cp\u003e8.5 Fault isolation and identification 324\u003c\/p\u003e \u003cp\u003e8.5.1 Diagnosis of step-type fault conditions 325\u003c\/p\u003e \u003cp\u003e8.5.2 Diagnosis of general deterministic fault conditions 328\u003c\/p\u003e \u003cp\u003e8.5.3 A simulation example 328\u003c\/p\u003e \u003cp\u003e8.6 Application study of a gearbox system 331\u003c\/p\u003e \u003cp\u003e8.6.1 Process description 332\u003c\/p\u003e \u003cp\u003e8.6.2 Fault description 332\u003c\/p\u003e \u003cp\u003e8.6.3 Identification of a monitoring model 334\u003c\/p\u003e \u003cp\u003e8.6.4 Detecting a fault condition 338\u003c\/p\u003e \u003cp\u003e8.7 Analysis of primary and improved residuals 341\u003c\/p\u003e \u003cp\u003e8.7.1 Central limit theorem 341\u003c\/p\u003e \u003cp\u003e8.7.2 Further statistical properties of primary residuals 344\u003c\/p\u003e \u003cp\u003e8.7.3 Sensitivity of statistics based on improved residuals 349\u003c\/p\u003e \u003cp\u003e8.8 Tutorial session 353\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Description of Modeling Methods 355\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Principal component analysis 357\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 The core algorithm 357\u003c\/p\u003e \u003cp\u003e9.2 Summary of the PCA algorithm 362\u003c\/p\u003e \u003cp\u003e9.3 Properties of a PCA model 363\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Partial least squares 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Preliminaries 375\u003c\/p\u003e \u003cp\u003e10.2 The core algorithm 377\u003c\/p\u003e \u003cp\u003e10.3 Summary of the PLS algorithm 380\u003c\/p\u003e \u003cp\u003e10.4 Properties of PLS 381\u003c\/p\u003e \u003cp\u003e10.5 Properties of maximum redundancy PLS 396\u003c\/p\u003e \u003cp\u003eReferences 410\u003c\/p\u003e \u003cp\u003eIndex 427 \u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402264191319,"sku":"9780470028193","price":62.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470028193.jpg?v=1730479879","url":"https:\/\/bookcurl.com\/products\/statistical-monitoring-of-complex-multivatiate-processes-9780470028193","provider":"Book Curl","version":"1.0","type":"link"}