Data-Driven Fault Detection and Reasoning for Industrial Monitoring
This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial proce...
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| Main Authors: | , , |
|---|---|
| Corporate Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Singapore :
Springer Nature Singapore : Imprint: Springer,
2022.
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| Edition: | 1st ed. 2022. |
| Series: | Intelligent Control and Learning Systems,
3 |
| Subjects: | |
| Online Access: | Link to Metadata |
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Table of Contents:
- Introduction
- Basic Statistical Fault Detection Problems
- Principal Component Analysis
- Canonical Variate Analysis
- Partial Least Squares Regression
- Fisher Discriminant Analysis
- Canonical Variate Analysis
- Fault Classification based on Local Linear Embedding
- Fault Classification based on Fisher Discriminant Analysis
- Quality-Related Global-Local Partial Least Square Projection Monitoring
- Locality-Preserving Partial Least-Squares Statistical Quality Monitoring
- Locally Linear Embedding Orthogonal Projection to Latent Structure (LLEPLS)
- Bayesian Causal Network for Discrete Systems
- Probability Causal Network for Continuous Systems
- Dual Robustness Projection to Latent Structure Method based on the L_1 Norm.