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Diagnosis of process nonlinearities and valve stiction : data driven approaches / M A A Shoukat Choudhury

By: Choudhury, M. A. AContributor(s): Choudhury, M. A. A | Thornhill, N. F | Shah, S. LMaterial type: Text

TextPublisher number: :Donated from Prof. J P Gupta Series: Advances in industrial controlPublication details: Berlin : Springer, ©2008Description: xxii, 284 pages 24cmISBN: 9783540792239Subject(s): Engineering | Automatic control -- Reliability | Nonlinear control theory | Valves -- Reliability | TECHNOLOGY & ENGINEERING -- Automation | TECHNOLOGY & ENGINEERING -- Robotics | IngénierieDDC classification: 629.836 CHO

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{̃u2013} 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.

Summary: n 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 loop performance. Often valve stiction is the main cause of poor control performance. A generalized definition of valve stiction based on the investigation of real plant data is proposed. A simple data-driven model of valve stiction is developed. The model is simple, yet powerful enough to properly simulate the complex valve stiction phenomena. Both open and closed loop results have been presented and validated to show the capability of the model. Conventional invasive methods such as the valve travel test can detect stiction easily. However, they are expensive, time consuming and tedious to use for examining thousands of valves in a typical process industry. A non-invasive method that can simultaneously detect and quantify control valve stiction is presented. The method requires only routine operating data from the process. Over a dozen industrial case studies have demonstrated the wide applicability and practicality of this method. In chemical industrial practice, data are often compressed for archival purposes, using various techniques. Compression degrades data quality and induces nonlinearity in the data. The issues of data quality degradation and nonlinearity induction due to compression are investigated in this book. An automatic method for detection and quantification of the compression present in the archived data is discussed. Compelling and quantitative analyses have been recommended to end the practice of process data compression.

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Item type	Current library	Call number	Status	Notes	Date due	Barcode	Item holds
Books	SNU LIBRARY	629.836 CHO (Browse shelf(Opens below))	Not For Loan	Donated from J P Gupta		G2581

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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{̃u2013} 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.

n 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 loop performance. Often valve stiction is the main cause of poor control performance. A generalized definition of valve stiction based on the investigation of real plant data is proposed. A simple data-driven model of valve stiction is developed. The model is simple, yet powerful enough to properly simulate the complex valve stiction phenomena. Both open and closed loop results have been presented and validated to show the capability of the model. Conventional invasive methods such as the valve travel test can detect stiction easily. However, they are expensive, time consuming and tedious to use for examining thousands of valves in a typical process industry. A non-invasive method that can simultaneously detect and quantify control valve stiction is presented. The method requires only routine operating data from the process. Over a dozen industrial case studies have demonstrated the wide applicability and practicality of this method. In chemical industrial practice, data are often compressed for archival purposes, using various techniques. Compression degrades data quality and induces nonlinearity in the data. The issues of data quality degradation and nonlinearity induction due to compression are investigated in this book. An automatic method for detection and quantification of the compression present in the archived data is discussed. Compelling and quantitative analyses have been recommended to end the practice of process data compression.

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