Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques (Paperback, Softcover reprint of hardcover 1st ed. 2002)

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.

General

Imprint:	Springer London
Country of origin:	United Kingdom
Series:	Advances in Industrial Control
Release date:	October 2010
First published:	2003
Authors:	Silvio Simani • Cesare Fantuzzi • Ron J. Patton
Dimensions:	235 x 155 x 15mm (L x W x T)
Format:	Paperback
Pages:	282
Edition:	Softcover reprint of hardcover 1st ed. 2002
ISBN-13:	978-1-84996-895-9
Categories:	Books > Professional & Technical > Mechanical engineering & materials > Mechanical engineering > General Books > Professional & Technical > Mechanical engineering & materials > Production engineering > Reliability engineering Books > Professional & Technical > Mechanical engineering & materials > Materials science > Mechanics of solids > Dynamics & vibration Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
LSN:	1-84996-895-0
Barcode:	9781849968959

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