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:
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A comprehensive coverage of Bayesian Inference for control system fault diagnosis.
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Theory and applications are self-contained.
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Provides detailed algorithms and sample Matlab codes.
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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.
