Abstract
Centrifugal pumps play vital role in many critical applications in industries and require continuous health monitoring to increase its availability. In centrifugal pumps, inlet pipe blockages and cavitation in the casing cause serious problems, such as the abnormal noise, high vibration, leakage, and decrease in the head capacity and the efficiency. Complexity in such fault is that one leads to other, and as such it falls under the multi-fault condition, which is hardly dealt in literature for pumps. Vibration-based monitoring of machinery faults using machine learning approaches has been one of latest trends. This paper presents the use of one of the machine learning tools, i.e., the support vector machine (SVM), for innovatively using in the diagnosis of blockage levels and the impeding cavitation at diverse pump speeds using statistical features extracted from vibration signature. SVM classifier parameters are optimally selected for better classifications. The prediction of classification is encouraging while taking decision for the pump condition.
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Acknowledgements
The SVM software package LIBSVM (2011), Version 3.1, has been used for the present multi-class classification and could be downloaded freely from the web-address http://www.csie.ntu.edu.tw/~cjlin/libsvm. The single objective genetic algorithm is used for optimizing the SVM parameters and can be downloaded freely from the web-address http://www.ise.ncsu.edu/kay. The artificial bee colony algorithm ABCA software package version 2 (2009) is used for optimizing SVM parameters and could be downloaded freely from the web-address http://www.mf.erciyes.edu.tr/abc. Authors thank all of them.
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Technical Editor: Kátia Lucchesi Cavalca Dedini.
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Bordoloi, D.J., Tiwari, R. Identification of suction flow blockages and casing cavitations in centrifugal pumps by optimal support vector machine techniques. J Braz. Soc. Mech. Sci. Eng. 39, 2957–2968 (2017). https://doi.org/10.1007/s40430-017-0714-z
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DOI: https://doi.org/10.1007/s40430-017-0714-z