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Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes (Paperback, 2008 ed.)
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Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes (Paperback, 2008 ed.)
Series: Lecture Notes in Control and Information Sciences, 377
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An unappealing characteristic of all real-world systems is the fact
that they are vulnerable to faults, malfunctions and, more
generally, unexpected modes of - haviour. This explains why there
is a continuous need for reliable and universal monitoring systems
based on suitable and e?ective fault diagnosis strategies. This is
especially true for engineering systems, whose complexity is
permanently growing due to the inevitable development of modern
industry as well as the information and communication technology
revolution. Indeed, the design and operation of engineering systems
require an increased attention with respect to availability,
reliability, safety and fault tolerance. Thus, it is natural that
fault diagnosis plays a fundamental role in modern control theory
and practice. This is re?ected in plenty of papers on fault
diagnosis in many control-oriented c- ferencesand journals.Indeed,
a largeamount of knowledgeon model basedfault diagnosis has been
accumulated through scienti?c literature since the beginning of the
1970s. As a result, a wide spectrum of fault diagnosis techniques
have been developed. A major category of fault diagnosis techniques
is the model based one, where an analytical model of the plant to
be monitored is assumed to be available
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