Author Description

<p><b>Process Control System Fault Diagnosis: A Bayesian Approach</b></p> <p>Ruben T. Gonzalez, University of Alberta, Canada</p> <p>Fei Qi, Suncor Energy Inc., Canada</p> <p>Biao Huang, University of Alberta, Canada</p> <p> </p> <p><b><i>Data-driven Inferential Solutions for Control System Fault Diagnosis</i></b></p> <p><b><i> </i></b></p> <p>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.</p> <p><i>Process Control System Fault Diagnosis: A Bayesian Approach</i> 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.</p> <p> </p> <p>Key features:</p> <p>• A comprehensive coverage of Bayesian Inference for control system fault diagnosis.</p> <p>• Theory and applications are self-contained.</p> <p>• Provides detailed algorithms and sample Matlab codes.</p> <p>• Theory is illustrated through benchmark simulation examples, pilot-scale experiments and industrial application.</p> <p> </p> <p><i>Process Control System Fault Diagnosis: A Bayesian Approach </i>is a comprehensive guide for graduate students, practicing engineers, and researchers who are interests in a