Diagnosis of Process Nonlinearities and Valve Stiction Data Driven Approaches /
In this book, Higher Order Statistical (HOS) theory is used to develop indices for detecting and quantifying signal non-Gaussianity and nonlinearity. These indices, together with specific patterns in the mapping of process output and controller output are used to diagnose the causes of poor control...
| Main Authors: | , , |
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| Corporate Author: | |
| Language: | English |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2008.
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| Edition: | 1st ed. 2008. |
| Series: | Advances in Industrial Control,
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-540-79224-6 |
Table of Contents:
- Higher-Order Statistics
- Higher-Order Statistics: Preliminaries
- Bispectrum and Bicoherence
- Data Quality – Compression and Quantization
- Impact of Data Compression and Quantization on Data-Driven Process Analyses
- Nonlinearity and Control Performance
- Measures of Nonlinearity – A Review
- Linear or Nonlinear? A Bicoherence-Based Measure of Nonlinearity
- A Nonlinearity Measure Based on Surrogate Data Analysis
- Nonlinearities in Control Loops
- Diagnosis of Poor Control Performance
- Control Valve Stiction~– Definition, Modelling, Detection and Quantification
- Different Types of Faults in Control Valves
- Stiction: Definition and Discussions
- Physics-Based Model of Control Valve Stiction
- Data-Driven Model of Valve Stiction
- Describing Function Analysis
- Automatic Detection and Quantification of Valve Stiction
- Industrial Applications of the Stiction Quantification Algorithm
- Confirming Valve Stiction
- Plant-wide Oscillations – Detection and Diagnosis
- Detection of Plantwide Oscillations
- Diagnosis of Plant-wide Oscillations.