Condition Monitoring Using Computational Intelligence

Condition Monitoring Using Computational Intelligence

Tshilidzi Marwala (University of the Witwatersrand, South Africa) and Christina Busisiwe Vilakazi (University of the Witwatersrand, South Africa)
DOI: 10.4018/978-1-59904-582-5.ch006
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Abstract

Condition 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 was tested using SVM, HMM, GMM, and ENN on condition monitoring of bearings and are found to give good results.

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