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- W2364450362 abstract "Aimed at the non-stationary characteristics of rolling bearing vibration signal,a fault diagnosis method was proposed based on local-wave method and KPCA(kernel principal component analysis)-LSSVM(least squares support vector machine).Firstly,local wave decomposition was used to decompose rolling bearing vibration signal into several intrinsic mode function(IMF),whose feature energy,singular values and AR model parameters were computed as initial feature vectors.Secondly,ini tial feature vectors were mapped into a higher-dimensional space with KPCA,and the kemel principal components were extracted as new feature vectors,which used as the input of LSSVM for fault classification.The experimental results show the KPCA-LSSVM method improves LSSVM's classification performance by KPCA obtaining additional discriminative information,and has better generalization than direct LSSVM method,and can identify rolling bearing fault patterns more accurately." @default.
- W2364450362 created "2016-06-24" @default.
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- W2364450362 date "2010-09-21" @default.
- W2364450362 modified "2023-09-26" @default.
- W2364450362 title "Rolling bearing fault diagnosis based on local wave method and KPCA-LSSVM" @default.
- W2364450362 doi "https://doi.org/10.3785/j.issn.1008-973x.2010.08.015" @default.
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