Artificial Intelligence Tools: Decision Support Systems in
Condition Monitoring and Diagnosis discusses various white- and
black-box approaches to fault diagnosis in condition monitoring
(CM). This indispensable resource: Addresses
nearest-neighbor-based, clustering-based, statistical, and
information theory-based techniques Considers the merits of each
technique as well as the issues associated with real-life
application Covers classification methods, from neural networks to
Bayesian and support vector machines Proposes fuzzy logic to
explain the uncertainties associated with diagnostic processes
Provides data sets, sample signals, and MATLAB (R) code for
algorithm testing Artificial Intelligence Tools: Decision Support
Systems in Condition Monitoring and Diagnosis delivers a thorough
evaluation of the latest AI tools for CM, describing the most
common fault diagnosis techniques used and the data acquired when
these techniques are applied.
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