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: ac fuzzy systems; rough and neuro-rough sets; neural and Bayesian networks;hidden Markov and Gaussian mixture models; and support vector machines.Applications in Mechanical and Electrical Systems Tshilidzi Marwala ... Jing and Vadakkepat (2009) applied a Markov Chain Monte Carlo technique to the tracking of maneuvering objects whereas Gallagher et al. ... In the Markov Chain Monte Carlo, the transition between states is achieved by adding random noise (aquot; ) to the current state as follows (Bishop 1995; Marwala ... (2011) applied the Metropolis algorithm for the magnetic phase diagram simulation of La1- xCaxMnO3 system byanbsp;...
|Title||:||Condition Monitoring Using Computational Intelligence Methods|
|Publisher||:||Springer Science & Business Media - 2012-01-25|