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Название: Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes (Lecture Notes in Control and Information Sciences, Volume 377)
Автор: Patan K.
An unappealing characteristic of all real-world systems is the fact that they are
vulnerable to faults, malfunctions and, more generally, unexpected modes of behaviour.
This explains why there is a continuous need for reliable and universal
monitoring systems based on suitable and effective 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 reflected in plenty of papers on fault diagnosis in many control-oriented conferences
and journals. Indeed, a large amount of knowledge on model based fault
diagnosis has been accumulated through scientific literature since the beginning
of the 1970s. As a result, a wide spectrum of fault diagnosis techniques have