Computational Intelligence for Condition Monitoring

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dc.creatorMarwala, Tshilidzi
dc.creatorVilakazi, Christina Busisiwe
dc.date2007-05-17
dc.date.accessioned2026-07-07T08:02:12Z
dc.date.available2026-07-07T08:02:12Z
dc.descriptionCondition monitoring techniques are described in this chapter. Two aspects of condition monitoring process are considered: (1) feature extraction; and (2) condition classification. Feature extraction methods described and implemented are fractals, Kurtosis and Mel-frequency Cepstral Coefficients. Classification methods described and implemented are support vector machines (SVM), hidden Markov models (HMM), Gaussian mixture models (GMM) and extension neural networks (ENN). The effectiveness of these features were tested using SVM, HMM, GMM and ENN on condition monitoring of bearings and are found to give good results.
dc.description23 pages
dc.identifierhttps://arxiv.org/abs/0705.2604
dc.identifierhttp://arxiv.org/abs/0705.2604
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/129155
dc.subjectComputational Engineering, Finance, and Science
dc.titleComputational Intelligence for Condition Monitoring
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