Journal
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
Volume 17, Issue 3, Pages 152-157Publisher
ELSEVIER - DIVISION REED ELSEVIER INDIA PVT LTD
DOI: 10.1016/j.jestch.2014.04.005
Keywords
Mono-block centrifugal pump; SVM algorithm; Fault diagnosis; Continuous wavelet transforms (CWT)
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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. Copyright (C) 2014, Karabuk University. Production and hosting by Elsevier B.V. All rights reserved.
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