Publication Type:

Journal Article

Source:

Asian Journal of Science and Applied Technology, Volume 17, Number 3, p.152 - 157 (2014)

URL:

http://www.sciencedirect.com/science/article/pii/S2215098614000275

Keywords:

Continuous wavelet transforms (CWT)

Abstract:

Abstract Monoblock centrifugal pumps are employed in variety of critical engineering applications. Continuous monitoring of such machine component becomes essential in order to reduce the unnecessary break downs. At the outset, vibration based approaches are widely used to carry out the condition monitoring tasks. Particularly fuzzy logic, support vector machine (SVM) and artificial neural networks were employed for continuous monitoring and fault diagnosis. In the present study, the application of \{SVM\} algorithm in the field of fault diagnosis and condition monitoring is discussed. The continuous wavelet transforms were calculated for different families and at different levels. The computed transformation coefficients form the feature set for the classification of good and faulty conditions of the components of centrifugal pump. The classification accuracies of different continuous wavelet families at different levels were calculated and compared to find the best wavelet for the fault diagnosis of the monoblock centrifugal pump.

Cite this Research Publication

V. Muralidharan, Sugumaran, V., Indira, V., and Dr. Sakthivel N.R., “Fault Diagnosis of Monoblock Centrifugal Pump Using Stationary Wavelet Features and Bayes Algorithm”, Asian Journal of Science and Applied Technology, vol. 17, pp. 152 - 157, 2014.

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