Condition Monitoring Using Computational Intelligence Methods promotes
the various approaches gathered under the umbrella of computational
intelligence to show how condition monitoring can be used to avoid
equipment failures and lengthen its useful life, minimize downtime and
reduce maintenance costs. The text introduces various signal-processing
and pre-processing techniques, wavelets and principal component
analysis, for example, together with their uses in condition monitoring
and details the development of effective feature extraction techniques
classified into frequency-, time-frequency- and time-domain analysis.
Data generated by these techniques can then be used for condition
classification employing tools such as:
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fuzzy systems; rough and neuro-rough sets; neural and Bayesian
networks;hidden Markov and Gaussian mixture models; and support vector
machines.