Fault detection and diagnosis in industrial systems
Author(s)
Bibliographic Information
Fault detection and diagnosis in industrial systems
（Advanced textbooks in control and signal processing）
Springer, 2001
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
Early and accurate fault detection and diagnosis for modern chemical plants can minimize downtime, increase the safety of plant operations, and reduce manufacturing costs. This book presents the theoretical background and practical techniques for data-driven process monitoring. It demonstrates the application of all the data-driven process monitoring techniques to the Tennessee Eastman plant simulator, and looks at the strengths and weaknesses of each approach in detail. A plant simulator and problems allow readers to apply process monitoring techniques.
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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