Process Control System Fault Diagnosis: A Bayesian Approach

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.