Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes
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...
| Main Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
London
Springer London
2000, 2000
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| Edition: | 1st ed. 2000 |
| Series: | Advances in Industrial Control
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- I. Introduction
- 1. Introduction
- II. Background
- 2. Multivariate Statistics
- 3. Pattern Classification
- III. Methods
- 4. Principal Component Analysis
- 5. Fisher Discriminant Analysis
- 6. Partial Least Squares
- 7. Canonical Variate Analysis
- IV. Application
- 8. Tennessee Eastman Process
- 9. Application Description
- 10. Results and Discussion
- V. Other Approaches
- 11. Overview of Analytical and Knowledge-based Approaches
- References