Ebook: Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes
- Tags: Industrial Chemistry/Chemical Engineering, Database Management, Data Structures, Control Robotics Mechatronics
- Series: Advances in Industrial Control
- Year: 2000
- Publisher: Springer-Verlag London
- Edition: 1
- Language: English
- pdf
Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis.
The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process-monitoring techniques to a nontrivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques.
The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application.
Content:
Front Matter....Pages I-XIII
Front Matter....Pages 1-1
Introduction....Pages 3-10
Front Matter....Pages 11-11
Multivariate Statistics....Pages 13-23
Pattern Classification....Pages 25-29
Front Matter....Pages 31-31
Principal Component Analysis....Pages 33-52
Fisher Discriminant Analysis....Pages 53-65
Partial Least Squares....Pages 67-80
Canonical Variate Analysis....Pages 81-95
Front Matter....Pages 97-97
Tennessee Eastman Process....Pages 99-108
Application Description....Pages 109-116
Results and Discussion....Pages 117-165
Front Matter....Pages 167-167
Overview of Analytical and Knowledge-Based Approaches....Pages 169-174
Back Matter....Pages 175-192
Content:
Front Matter....Pages I-XIII
Front Matter....Pages 1-1
Introduction....Pages 3-10
Front Matter....Pages 11-11
Multivariate Statistics....Pages 13-23
Pattern Classification....Pages 25-29
Front Matter....Pages 31-31
Principal Component Analysis....Pages 33-52
Fisher Discriminant Analysis....Pages 53-65
Partial Least Squares....Pages 67-80
Canonical Variate Analysis....Pages 81-95
Front Matter....Pages 97-97
Tennessee Eastman Process....Pages 99-108
Application Description....Pages 109-116
Results and Discussion....Pages 117-165
Front Matter....Pages 167-167
Overview of Analytical and Knowledge-Based Approaches....Pages 169-174
Back Matter....Pages 175-192
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