Process control system fault diagnosis : a bayesian approach First edition.

Author

• Gonzalez, Ruben

Additional Author(s)

• Huang, Biao
• Qi, Fei

Publisher

West Sussex: John Wiley & Sons Ltd, 2016

Language

English

ISBN

9781118770603

Series

Subject(s)

• BAYESIAN STATISTICAL DECISION THEORY
• CHEMICAL PROCESS CONTROL--STATISTICAL METHODS
• FAULT LOCATION (ENGINEERING)

Notes

• Includes bibliographical references and index (329-331)

. .

Abstract

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.

Physical Dimension

Number of Page(s)

1 online resource (xxvi, 331 p.)

Dimension

-

Other Desc.

-

Summary / Review / Table of Content

Front Matter (Pages: i-xxvi)

One : FUNDAMENTALS
CHAPTER 1 Introduction (Pages: 1-18)
CHAPTER 2 Prerequisite Fundamentals (Pages: 19-61)
CHAPTER 3 Bayesian Diagnosis (Pages: 62-67)
CHAPTER 4 Accounting for Autodependent Modes and Evidence (Pages: 68-82)
CHAPTER 5 Accounting for Incomplete Discrete Evidence (Pages: 83-95)
CHAPTER 6 Accounting for Ambiguous Modes: A Bayesian Approach (Pages: 96-111)
CHAPTER 7 Accounting for Ambiguous Modes: A Dempster–Shafer Approach (Pages: 112-125)
CHAPTER 8 Making use of Continuous Evidence Through Kernel Density Estimation (Pages: 126-143)
CHAPTER 9 Accounting for Sparse Data Within a Mode (Pages: 144-171)
CHAPTER 10 Accounting for Sparse Modes Within the Data (Pages: 172-199)

Two : APPLICATIONS
CHAPTER 11 Introduction to Testbed Systems (Pages: 201-208)
CHAPTER 12 Bayesian Diagnosis with Discrete Data (Pages: 209-220)
CHAPTER 13 Accounting for Autodependent Modes and Evidence (Pages: 221-231)
CHAPTER 14 Accounting for Incomplete Discrete Evidence (Pages: 232-246)
CHAPTER 15 Accounting for Ambiguous Modes in Historical Data: A Bayesian Approach (Pages: 247-271)
CHAPTER 16 Accounting for Ambiguous Modes in Historical Data: A Dempster–Shafer Approach (Pages: 272-287)
CHAPTER 17 Making use of Continuous Evidence through Kernel Density Estimation (Pages: 288-312)
CHAPTER 18 Dynamic Application of Continuous Evidence and Ambiguous Mode Solutions (Pages: 313-327)

Index (Pages: 329-331)

Exemplar(s)

#	Accession No.	Call Number	Location	Status
1.	01060/19	660.2815 Gon P	Online !	Available