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- W2348916448 abstract "To realize intelligent fault diagnosis of rolling bearings,a novel fault diagnosis approach based on Empirical Mode Decomposition(EMD) and Immune Parameter Adaptive Support Vector Machine(IPA-SVM) was proposed.The fault signal was decomposed into the sum of a number of Intrinsic Mode Functions(IMFs) by EMD.Through fault characteristic frequency filter function,the fault feature vectors were extracted from the envelope spectrum of IMFs automatically.On the basis of feature extraction,an improved immune clonal selection algorithm was introduced to optimize parameters of SVM with K-fold cross validation method,and the IPA-SVM classifier was obtained after sample training.SKF6203 rolling bearing data experiments demonstrated that the superior fault recognition rate could be acquired by proposed method." @default.
- W2348916448 created "2016-06-24" @default.
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- W2348916448 date "2013-01-01" @default.
- W2348916448 modified "2023-09-27" @default.
- W2348916448 title "Fault diagnosis of rolling bearings based on EMD and parameter adaptive support vector machine" @default.
- W2348916448 hasPublicationYear "2013" @default.
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