Wavelet decomposition and support vector machine for fault diagnosis of monoblock centrifugal pump
AbstractMonoblock centrifugal pumps play a very critical role in a variety of applications and condition monitoring of the various mechanical components of centrifugal pump becomes essential which in turn increases the productivity and reduces the breakdowns. Vibration-based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly, artificial neural networks fuzzy logic was employed for continuous monitoring and fault diagnosis. This paper presents the use of support vector machine (SVM) algorithm for fault diagnosis through discrete wavelet features extracted from vibration signals of good and faulty conditions of the components of centrifugal pump. The classification accuracies were computed for different types of classifiers such as artificial neural network (ANN), support vector machine (SVM) and J48 decision tree algorithm.
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Bibliographic InfoArticle provided by Inderscience Enterprises Ltd in its journal Int. J. of Data Analysis Techniques and Strategies.
Volume (Year): 3 (2011)
Issue (Month): 2 ()
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Web page: http://www.inderscience.com/browse/index.php?journalID=282
wavelet decomposition; support vector machines; SVM; classification; monoblock centrifugal pumps; condition monitoring; fault diagnosis; vibration signals; artificial neural networks; ANNs; decision trees.;
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