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Author:   L.H. Chiang ,  E.L. Russell ,  R.D. Braatz
Publisher:   Springer London Ltd
Imprint:   Springer London Ltd
Edition:   Softcover reprint of the original 1st ed. 2001
Dimensions:   Width: 15.50cm , Height: 1.50cm , Length: 23.50cm
Weight:   0.920kg
ISBN:  

9781852333270

ISBN 10:   1852333278
Pages:   279
Publication Date:   11 December 2000
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Awaiting stock  
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Table of Contents

I. Introduction.- 1. Introduction.- Process Monitoring Procedures.- Process Monitoring Measures.- Process Monitoring Methods.- Book Organization.- II. Background.- 2. Multivariate Statistics.- Data Pretreatment.- Univariate Statistical Monitoring.- T2 Statistic.- Thresholds for the T2 Statistic.- Data Requirements.- Homework Problems.- 3. Pattern Classification.- Discriminant Analysis.- Feature Extraction.- Homework Problems.- III. Data-driven Methods.- 4. Principal Component Analysis.- Principal Component Analysis.- Reduction Order.- Fault Detection.- Fault Identification.- Fault Diagnosis.- Dynamic PCA.- Other PCA-based Methods.- Homework Problems.- 5. Fisher Discriminant Analysis.- Fisher Discriminant Analysis.- Reduction Order.- Fault Detection and Diagnosis.- Comparison of PCA and FDA.- Dynamic FDA.- Homework Problems.- 6. Partial Least Squares.- PLS Algorithms.- Reduction Order and PLS Prediction.- Fault Detection, Identification, and Diagnosis.- Comparison of PCA and PLS.- Other PLS Methods.- Homework Problems.- 7. Canonical Variate Analysis.- CVA Theorem.- CVA Algorithm.- State Space Model and System Identifiability.- Lag Order Selection and Computation.- State Order Selection and Akaike's Information Criterion.- Subspace Algorithm Interpretations.- Process Monitoring Statistics.- Homework Problems.- IV. Application.- 8. Tennessee Eastman Process.- Process Flowsheet.- Process Variables.- Process Faults.- Simulation Program.- Control Structure.- Homework Problems.- 9. Application Description.- Data Sets.- Sampling Interval.- Sample Size.- Lag and Order Selection.- Fault Detection.- Fault Identification.- Fault Diagnosis.- 10. Results and Discussion.- Case Study on Fault.- Case Study on Fault 4.- Case Study on Fault 5.- Case Study on Fault 11.- Fault Detection.- Fault Identification.- Fault Diagnosis.- Homework Problems.- V. Analytical and Knowledge-based Methods.- 11. Analytical Methods.- Fault Descriptions.- Parameter Estimation.- Observer-based Method.- Full-order State Estimator.- Reduced-order Unknown Input Observer.- Parity Relations.- Residual Generation.- Detection Properties of the Residual.- Specification of the Residuals.- Implementation of the Residuals.- Connection Between the Observer and Parity Relations.- Isolation Properties of the Residual.- Residual Evaluation.- Homework Problems.- 12. Knowledge-based Methods.- Causal Analysis.- Signed Directed Graph.- Symptom Tree Model.- Expert Systems.- Shallow-Knowledge Expert System.- Deep-Knowledge Expert Systems.- Combination of Shallow-Knowledge and Deep-Knowledge Expert Systems.- Machine Learning Techniques.- Knowledge Representation.- Inference Engine.- Pattern Recognition.- Artificial Neural Networks.- Self-Organizing Map.- Combinations of Various Techniques.- Neural Networks and Expert Systems.- Fuzzy Logic.- Fuzzy Expert Systems.- Fuzzy Neural Networks.- Fuzzy Signed Directed Graph.- Fuzzy Logic and the Analytical Approach.- Neural Networks and the Analytical Approach.- Data-driven, Analytical, and Knowledge-based Ap- proaches.- Homework Problems.- References.

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