Fault Diagnosis of Metallurgical Machinery Based on Spectral Kurtosis and GA-SVM
2013 ◽
Vol 634-638
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pp. 3958-3961
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Keyword(s):
This paper proposed a new method of rolling element bearing (REB) fault diagnosis for metallurgical machinery. Mainly it stresses on the combination of spectral kurtosis (SK) and supports vector machine (SVM), using genetic algorithm (GA) to optimize the parameters of support vector machine at the same time. Thus, this study aims to integrate SK, GA and SVM in order to develop an intelligent REB fault detector for metallurgical machineries. Simulation study indicates that this method can effectively detect the REB faults with a high accuracy.
2015 ◽
Vol 230
(13)
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pp. 2314-2322
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2019 ◽
Vol 41
(14)
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pp. 4013-4022
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2014 ◽
Vol 687-691
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pp. 3569-3573
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2016 ◽
Vol 24
(2)
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pp. 272-282
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2014 ◽
Vol 22
(12)
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pp. 2921-2937
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