Diagnosis of Process Nonlinearities and Valve Stiction
Data Driven Approaches
Buch in deiner Nähe kaufen
Beschreibung
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 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.
were published in the series as the contributed volume, Process Control Performance Assessment: From Theory to Implementation with Andrzej Ordys, Damian Uduehi, and Michael Johnson as Editors (ISBN 978-1-84628-623-0, 2007). Along with this good progress in process controller assessment methods, researchers have also been investigating techniques to diagnose what is causing the process or control loop degradation. This requires the use of on-line data to identify faults via new diagnostic indicators of typical process problems. A significant focus of some of this research has been the issue of valve problems; a research direction that has been motivated by some industrial statistics that show up to 40% of control loops having performance degradation attributable to valve problems. Shoukat Choudhury, Sirish Shah, and Nina Thornhill have been very active in this research field for a number of years and have written a coherent and consistent presentation of their many research results as this monograph, Diagnosis of Process Nonlinearities and Valve Stiction. The Advances in Industrial Control series is pleased to welcome this new and substantial contribution to the process diagnostic literature. The reader will find the exploitation of the extensive process data archives created by today’s process computer systems one theme in the monograph. From another viewpoint, the use of higher-order statistics could be considered to provide a continuing link to the earlier methods of the statistical process control paradigm.
Analyses three important causes of performance deterioration common in process control and is of significant practical value in process plant troubleshooting Includes supplementary material: sn.pub/extras
Autor*in
Ali Ahammad Shoukat Choudhury
Themen in »Diagnosis of Process Nonlinearities and Valve Stiction«
Compression Diagnose Higher Order Statistics Stiction Valves control linearity modeling nonlinearity quality signal processing quality control, reliability, safety and risk
Stimmen zu »Diagnosis of Process Nonlinearities and Valve Stiction«
From the reviews:
“This monograph is aimed at researchers and practicing engineers interested in the diagnosis of closed-loop system performance. The goal is to present techniques for monitoring the performance of continuous processes in the chemical industry using process data. The material consolidates in one place some recent results concerned with the detection, diagnosis, and quantification of process nonlinearities.” (IEEE Control Systems Magazine, Vol. 29, October, 2009)
()