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This book provides novel approach to the diagnosis of complex
technical systems that are widely used in various kinds of
transportation, energy, metallurgy, metalworking, fuels, mining,
chemical, paper industries, etc. Effective diagnostic systems are
necessary for the early detection of errors in mechatronic systems,
for the organization of maintenance and for the assessment of the
performed service quality. Unfortunately, the practical use of AI
in the diagnosis of mechatronic systems is still quite limited and
the inability to build effective mechatronic systems leads to
significant economic losses and dangers. The main aim of this book
is to contribute to knowledge within the topic of diagnostics of
mechatronic systems by the analysis of the elements reliability
characteristics, using methods, models and algorithms for
diagnostics and by studying examples of model diagnostic systems
using AI methods based on neural networks, fuzzy inference systems
and genetic algorithms.
This book provides novel approach to the diagnosis of complex
technical systems that are widely used in various kinds of
transportation, energy, metallurgy, metalworking, fuels, mining,
chemical, paper industries, etc. Effective diagnostic systems are
necessary for the early detection of errors in mechatronic systems,
for the organization of maintenance and for the assessment of the
performed service quality. Unfortunately, the practical use of AI
in the diagnosis of mechatronic systems is still quite limited and
the inability to build effective mechatronic systems leads to
significant economic losses and dangers. The main aim of this book
is to contribute to knowledge within the topic of diagnostics of
mechatronic systems by the analysis of the elements reliability
characteristics, using methods, models and algorithms for
diagnostics and by studying examples of model diagnostic systems
using AI methods based on neural networks, fuzzy inference systems
and genetic algorithms.
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