Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques. Simani, S.; Fantuzzi, C.; and Patton, R. Springer, London, Berlin, 2002.
abstract   bibtex   
Safety in industrial process and production plants is a concern of rising importance, especially if people would be endangered by a catastrophic system failure. On the other hand, because the control devices which are now exploited to improve the overall performance of industrial processes include both sophisticated digital system design techniques and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of failure. As a direct consequence of this, control systems must include automatic supervision of closed-loop operation to detect and isolate malfunctions as early as possible. One of the most promising methods for solving this problem is the ``analytical redundancy'' approach, in which residual signals are obtained. The basic idea consists of using an accurate model of the system to mimic the real process behaviour. If a fault occurs, the residual signal, i.e., the difference between real system and model behaviours, can be used to diagnose and isolate the malfunction. This book focuses on model identification oriented to the analytical approach of fault diagnosis and identification. The problem is treated in all its aspects covering: choice of model structure; parameter identification; residual generation; fault diagnosis and isolation. Sample case studies are used to demonstrate the application of these techniques. Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques will be of interest to researchers in control and fault identification. Industrial control engineers interested in applying the latest methods in fault diagnosis will benefit from the practical examples and case studies.
@Book{simani-et-al:2002,
  author =	 {Simani, S. and Fantuzzi, C. and Patton, R.J.},
  title = 	 {Model-based Fault Diagnosis in Dynamic Systems  Using Identification 
Techniques},
  publisher = 	 {Springer},
  year = 	 2002,
  id =           200212,
  series =	 {Advances in Industrial Control},
  address =	 {London, Berlin},
  isbn =	 {1-85233-685-4},
  abstract ={Safety in industrial process and production plants is a
concern of rising importance, especially if people would be endangered
by a catastrophic system failure. On the other hand, because the
control devices which are now exploited to improve the overall
performance of industrial processes include both sophisticated digital
system design techniques and complex hardware (input-output sensors,
actuators, components and processing units), there is an increased
probability of failure. As a direct consequence of this, control
systems must include automatic supervision of closed-loop operation to
detect and isolate malfunctions as early as possible. One of the most
promising methods for solving this problem is the ``analytical
redundancy'' approach, in which residual signals are obtained. The
basic idea consists of using an accurate model of the system to mimic
the real process behaviour. If a fault occurs, the residual signal,
i.e., the difference between real system and model behaviours, can be
used to diagnose and isolate the malfunction. This book focuses on
model identification oriented to the analytical approach of fault
diagnosis and identification. The problem is treated in all its
aspects covering: choice of model structure; parameter identification;
residual generation; fault diagnosis and isolation. Sample case
studies are used to demonstrate the application of these
techniques. Model-based Fault Diagnosis in Dynamic Systems Using
Identification Techniques will be of interest to researchers in
control and fault identification. Industrial control engineers
interested in applying the latest methods in fault diagnosis will
benefit from the practical examples and case studies.}  }
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