Introduction to Condition Monitoring.- Data Gathering Methods.- Preprocessing and Feature Selection.- Condition Monitoring Using Neural Networks.- Condition Monitoring Using Support Vector Machines.- Condition Monitoring Using Neuro-fuzzy Methods.- Condition Monitoring Using Neuro-rough Methods.- Condition Monitoring Using Hidden Markov Models and Gaussian Mixture Models.- Condition Monitoring Using Hybrid Techniques.- Condition Monitoring Using Incremental Learning with Genetic Algorithms.- Conclusion.
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:
. fuzzy systems; rough and neuro-rough sets; neural and Bayesian networks;hidden Markov and Gaussian mixture models; and support vector machines.
Helps the practitioner prevent machine failure, improving safety and cutting costs
Shows the reader how computational intelligence can provide an efficacious alternative to traditional visual inspection techniques
Uses on-line learning methods to avoid the need for time-consuming systems retraining