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Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques

  • Book
  • © 2003

Overview

Authors:
  1. Silvio Simani

    1. Dipartimento di Ingegneria, Università di Ferrara, Ferrara, Italia

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  2. Cesare Fantuzzi

    1. Dipartimento di Scienze per l’Ingegneria, Università di Modena e Reggio Emilia, Italia

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  3. Ronald Jon Patton

    1. School of Engineering, The University of Hull, Kingston-Upon-Hull, UK

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  • The reader will gain practical knowledge of how to apply the latest models and techniques in fault diagnosis to industrial systems
  • Application of the subject matter will reduce the risk of failure in safety-critical systems
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advances in Industrial Control (AIC)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xv
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  2. Introduction

    • Silvio Simani, Cesare Fantuzzi, Ronald Jon Patton
    Pages 1-18

  3. Model-Based Fault Diagnosis Techniques

    • Silvio Simani, Cesare Fantuzzi, Ronald Jon Patton
    Pages 19-60

  4. System Identification for Fault Diagnosis

    • Silvio Simani, Cesare Fantuzzi, Ronald Jon Patton
    Pages 61-113

  5. Residual Generation, Fault Diagnosis and Identification

    • Silvio Simani, Cesare Fantuzzi, Ronald Jon Patton
    Pages 115-156

  6. Fault Diagnosis Application Studies

    • Silvio Simani, Cesare Fantuzzi, Ronald Jon Patton
    Pages 157-250

  7. Concluding Remarks

    • Silvio Simani, Cesare Fantuzzi, Ronald Jon Patton
    Pages 251-259

  8. Back Matter

    Pages 261-282
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Keywords

About this book

Safety in industrial process and production plants is a concern of rising importance but because the control devices which are now exploited to improve the performance of industrial processes include both sophisticated digital system design techniques and complex hardware, there is a higher probability of failure. Control systems must include automatic supervision of closed-loop operation to detect and isolate malfunctions quickly. A promising method for solving this problem is "analytical redundancy", in which residual signals are obtained and an accurate model of the system mimics real process behaviour. If a fault occurs, the residual signal is used to diagnose and isolate the malfunction. This book focuses on model identification oriented to the analytical approach of fault diagnosis and identification covering: choice of model structure; parameter identification; residual generation; and fault diagnosis and isolation. Sample case studies are used to demonstrate the application of these techniques.

Authors and Affiliations

  • Dipartimento di Ingegneria, Università di Ferrara, Ferrara, Italia

    Silvio Simani

  • Dipartimento di Scienze per l’Ingegneria, Università di Modena e Reggio Emilia, Italia

    Cesare Fantuzzi

  • School of Engineering, The University of Hull, Kingston-Upon-Hull, UK

    Ronald Jon Patton

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