Process Control System Fault Diagnosis (eBook)

A Bayesian Approach

Ruben Gonzalez, Fei Qi, Biao Huang (Autoren)

eBook Download: EPUB

2016
John Wiley & Sons (Verlag)
978-1-118-77059-7 (ISBN)

Infos

Process Control System Fault Diagnosis: A Bayesian Approach

Ruben T. Gonzalez, University of Alberta, Canada

Fei Qi, Suncor Energy Inc., Canada

Biao Huang, University of Alberta, Canada

 

Data-driven Inferential Solutions for Control System Fault Diagnosis

 

A typical modern process system consists of hundreds or even thousands of control loops, which are overwhelming for plant personnel to monitor. The main objectives of this book are to establish a new framework for control system fault diagnosis, to synthesize observations of different monitors with a prior knowledge, and to pinpoint possible abnormal sources on the basis of Bayesian theory.

Process Control System Fault Diagnosis: A Bayesian Approach consolidates results developed by the authors, along with the fundamentals, and presents them in a systematic way. The book provides a comprehensive coverage of various Bayesian methods for control system fault diagnosis, along with a detailed tutorial. The book is useful for graduate students and researchers as a monograph and as a reference for state-of-the-art techniques in control system performance monitoring and fault diagnosis. Since several self-contained practical examples are included in the book, it also provides a place for practicing engineers to look for solutions to their daily monitoring and diagnosis problems.

 

Key features:

•             A comprehensive coverage of Bayesian Inference for control system fault diagnosis.

•             Theory and applications are self-contained.

•             Provides detailed algorithms and sample Matlab codes.

•             Theory is illustrated through benchmark simulation examples, pilot-scale experiments and industrial application.

 

Process Control System Fault Diagnosis: A Bayesian Approach is a comprehensive guide for graduate students, practicing engineers, and researchers who are interests in applying theory to practice.

Ruben Gonzalez completed his Bachelor's degree in chemical engineering in 2008 at the University of New Brunswick. Under the supervision of Dr. Biao Huang, he completed his Master's degree in 2010 and his Doctorate in 2014, both in chemical engineering, at the University of Alberta. His research interests include Bayesian diagnosis, fault detection and diagnosis, data reconciliation, and applied kernel density estimation.
Fei Qi obtained his Ph.D. degree in Process Control from the University of Alberta, Canada, in 2011. He had his M.Sc. degree (2006) and B.Sc. degree (2003) in Automation from the University of Science and Technology of China. Fei Qi joined Suncor Energy Inc. in 2010 as an Advance Process Control Engineer. He has extensive experiences in applying system identification, model predictive control, and control performance monitoring in real industrial processes. His Ph.D. research was on applying Bayesian statistics to control loop diagnosis. His current research interests include model predictive control, soft sensor, fault detection, and process optimization.
Biao Huang obtained his PhD degree in Process Control from the University of Alberta, Canada, in 1997. He is currently a Professor in the Department of Chemical and Materials Engineering, University of Alberta, NSERC Industrial Research Chair in Control of Oil Sands Processes and AITF Industry Chair in Process Control. He is a Fellow of the Canadian Academy of Engineering, Fellow of the Chemical Institute of Canada, and recipient of numerous awards including Germany's Alexander von Humboldt Research Fellowship, Bantrel Award in Design and Industrial Practice, APEGA Summit Award in Research Excellence, best paper award from Journal of Process Control etc. Biao Huang's main research interests include: Bayesian inference, control performance assessment, fault detection and isolation. Biao Huang has applied his expertise extensively in industrial practice. He also serves as the Deputy Editor-in-Chief for Control Engineering Practice, the Associate Editor for Canadian Journal of Chemical Engineering and the Associate Editor for Journal of Process Control.

Textauszug

Preface

Background

Control performance monitoring (CPM) has been and continues to be one of the most active research areas in the process control community. A number of CPM technologies have been developed since the late 1980s. It is estimated that several hundred papers have been published in this or related areas. CPM techniques have also been widely applied in industry. A number of commercial control performance assessment software packages are available off the shelf.

CPM techniques include controller monitoring, sensor monitoring, actuator monitoring, oscillation detection, model validation, nonlinearity detection and so on. All of these techniques have been designed to target a specific problem source in a control system. The common practice is that one monitoring technique (or monitor) is developed for a specific problem source. However, a specific problem source can show its signatures in more than one monitor, thereby inducing alarm flooding. There is a need to consider all monitors simultaneously in a systematic manner.

There are a number of challenging issues:

1. There are interactions between monitors. A monitor cannot be designed to just monitor one problem source in isolation from other problem sources. While each monitor may work well when only the targeted problem occurs, relying on a single monitor can be misleading when other problems also occur.
2. The causal relations between a problem source and a monitor are not obvious for industrial-scale problems. First-principles knowledge, including the process flowchart, cannot always provide an accurate causal relation.
3. Disturbances and uncertainties exist everywhere in industrial settings.
4. Most monitors are either model-based or data-driven; it is uncommon for monitor results to be combined with prior process knowledge.

Clearly, there is a need to develop a systematic framework, including theory and practical guidelines, to tackle the these monitoring problems.

Control Performance Diagnosis and Control System Fault Diagnosis

Control systems play a critical role in modern process industries. Malfunctioning components in control systems, including sensors, actuators and other components, are not uncommon in industrial environments. Their effects introduce excess variation throughout the process, thereby reducing machine operability, increasing costs and emissions, and disrupting final product quality control. It has been reported in the literature that as many as 60% of industrial controllers may have some kind of problem.

The motivation behind this book arises from the important task of isolating and diagnosing control performance abnormalities in complex industrial processes. A typical modern process operation consists of hundreds or even thousands of control loops, which is too many for plant personnel to monitor. Even if poor performance is detected in some control loops, because a problem in a single component can invoke a wide range of control problems, locating the underlying problem source is not a trivial task. Without an advanced information synthesis and decision-support system, it is difficult to handle the flood of process alarms to determine the source of the underlying problem. Human beings' inability to synthesize high-dimensional process data is the main reason behind these problems. The purpose of control performance diagnosis is to provide an automated procedure that aids plant personnel to determine whether specified performance targets are being met, evaluate the performance of control loops, and suggest possible problem sources and a troubleshooting sequence.

To understand the development of control performance diagnosis, it is necessary to review the historical evolution of CPM. From the 1990s and 2000s, there was a significant development in CPM and, from the 2000s to the 2010s, control performance diagnosis. CPM focuses on determining how well the controller is performing with respect to a given benchmark, while CPD focuses on diagnosing the causes of poor performance. CPM and CPD are of significant interest for process industries that have growing safety, environmental and efficiency requirements. The classical method of CPM was first proposed in 1989 by Harris, who used the minimum variance control (MVC) benchmark as a general indicator of control loop performance. The MVC benchmark can be obtained using the filtering and correlation (FCOR) algorithm, as proposed by Huang et al. in 1997; this technique can be easily generalized to obtain benchmarks for multivariate systems. Minimum variance control is generally aggressive, with potential for poor robustness, and is not a suitable benchmark for CPM of model predictive control, as itdoes not take input action into account. Thus the linear quadratic Gaussian (LQG) benchmark was proposed in the PhD dissertation of Huang in 1997. In order to extend beyond simple benchmark comparisons, a new family of methods was developed to monitor specific instruments within control loops for diagnosing poor performance (by Horch, Huang, Jelali, Kano, Qin, Scali, Shah, Thornhill, etc). As a result, various CPD approaches have appeared since 2000.

To address the CPD problem systematically, Bayesian diagnosis methods were introduced by Huang in 2008. Due to their ability to incorporate both prior knowledge and data, Bayesian methods are a powerful tool for CPD. They have been proven to be useful for a variety of monitoring and predictive maintenance purposes. Successful applications of the Bayesian approach have also been reported in medical science, image processing, target recognition, pattern matching, information retrieval, reliability analysis and engineering diagnosis. It provides a flexible structure for modelling and evaluating uncertainties. In the presence of noise and disturbances, Bayesian inference provides a good way to solve the monitoring and diagnosis problem, providing a quantifiable measure of uncertainty for decision making. It is one of the most widely applied techniques in statistical inference, as well being used to diagnose engineering problems.

The Bayesian approach was applied to fault detection and diagnosis (FDI) in the mechanical components of transport vehicles by Pernestal in 2007, and Huang applied it to CPD in 2008. CPD techniques bear some resemblance to FDI. Faults usually refer to failure events, while control performance abnormality does not necessarily imply a failure. Thus, CPD is performance-related, often focusing on detecting control related problems that affect control system performance, including economic and environmental performance, while FDI focuses on the failure of components. Under the Bayesian framework, both can be considered as an abnormal event or fault diagnosis for control systems. Thus control system fault diagnosis is a more appropriate term that covers both.

Book Objective, Organization and Readership

The main objectives of this book are to establish a Bayesian framework for control system fault diagnosis, to synthesize observations of different monitors with prior knowledge, and to pinpoint possible abnormal sources on the basis of Bayesian theory. To achieve these objectives, this book provides comprehensive coverage of various Bayesian methods for control system fault diagnosis. The book starts with a tutorial introduction of Bayesian theory and its applications for general diagnosis problems, and an introduction to the existing control loop performance-monitoring techniques. Based upon these fundamentals, the book turns to a general data-driven Bayesian framework for control system fault diagnosis. This is followed by presentation of various practicalproblems and solutions. To extend beyond traditional CPM with discrete outputs, this book also explores how control loop performance monitors with continuous outputs can be directly incorporated into the Bayesian diagnosis framework, thus improving diagnosis performance. Furthermore, to deal with historical data taken from ambiguous operating conditions, two approaches are explored:

• Dempster–Shafer theory, which is often used in other applications when ambiguity is present
• a parametrized Bayesian approach.

Finally, to demonstrate the practical relevance of the methodology, the proposed solutions are demonstrated through a number of practical engineering examples.

This book attempts to consolidate results developed or published by the authors over the last few years and to compile them together with their fundamentals in a systematic way. In this respect, the book is likely to be of use for graduate students and researchers as a monograph, and as a place to look for basic as well as state-of-the-art techniques in control system performance monitoring and fault diagnosis. Since several self-contained practical examples are included in the book, it also provides a place for practising engineers to look for solutions to their daily monitoring and diagnosis problems. In addition, the book has comprehensive coverage of Bayesian theory and its application in fault diagnosis, and thus it will be of interest to mathematically oriented readers who are interested in applying theory to practice. On the other hand, due to the combination of theory and applications, it will also be beneficial to applied researchers and practitioners who are interested in giving themselves a sound theoretical foundation. The readers of this book will include graduate students and researchers in chemical engineering, mechanical engineering and electrical engineering, specializing in process control, control systems and process systems engineering. It is expected that readers will be acquainted with some fundamental knowledge of undergraduate...

Produkt-Details

• Erscheint lt. Verlag: 25.7.2016
• Reihe/Serie: Wiley Series in Dynamics and Control of Electromechanical Systems
• Reihe/Serie: Wiley Series in Dynamics and Control of Electromechanical Systems
• Reihe/Serie: Wiley Series in Dynamics and Control of Electromechanical Systems
• Sprache: englisch
• Themenwelt: Naturwissenschaften ► Chemie ► Technische Chemie
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Themenwelt: Technik ► Maschinenbau
• Schlagworte: Ambiguous Data • Bayesian Methods • Bayessches Verfahren • chemical engineering • Chemische Verfahrenstechnik • Control Performance Assessment • Control Process & Measurements • Control System Monitoring • Control System Safety • Control Systems Technology • Dempster-Shafer Theory • Electrical & Electronics Engineering • Elektrotechnik u. Elektronik • Fault Diagnosis • Information Synthesis • Kernel Density Estimation • Maschinenbau • mechanical engineering • Mess- u. Regeltechnik • missing data • Process Engineering • Prozesssteuerung • Regelungstechnik
• ISBN-10: 1-118-77059-5 / 1118770595
• ISBN-13: 978-1-118-77059-7 / 9781118770597