Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques

by Silvio Simani, Cesare Fantuzzi, Ron J. Patton

Overview

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.

Product Details

ISBN-13:	9781447138297
Publisher:	Springer-Verlag New York, LLC
Publication date:	11/11/2013
Series:	Advances in Industrial Control
Sold by:	Barnes & Noble
Format:	eBook
File size:	10 MB

Table of Contents

1. Introduction.- 2. Model-based Fault Diagnosis Techniques.- 3. System Identification for Fault Diagnosis.- 4. Residual Generation, Fault Diagnosis and Identification.- 5. Fault Diagnosis Application Studies.- 6. Concluding Remarks.- References.