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Condition Monitoring Using Support Vector Machines and Extension Neural Networks Classifiers

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Condition Monitoring Using Computational Intelligence Methods

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Abstract

Feature extraction and condition classification are considered in this chapter. The Feature extraction approaches applied in this chapter are fractals, Kurtosis and Mel-frequency Cepstral Coefficients. The classification approaches applied in this chapter are Support Vector Machines (SVMs) and Extension Neural Networks (ENNs). The usefulness of these features were tested with SVMs and ENNs for the condition monitoring of bearings and were found to give good results.

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  1. Faculty of Engineering and the Built Environment, University of Johannesburg, Auckland Park, Johannesburg, South Africa

    Tshilidzi Marwala

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Marwala, T. (2012). Condition Monitoring Using Support Vector Machines and Extension Neural Networks Classifiers. In: Condition Monitoring Using Computational Intelligence Methods. Springer, London. https://doi.org/10.1007/978-1-4471-2380-4_10

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