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• W2946480175 abstract "Aimed at distinguishing different fault categories of severity of rolling bearings, a novel method based on feature space reconstruction and multiscale permutation entropy is proposed in the study. Firstly, the ensemble empirical mode decomposition algorithm (EEMD) was employed to adaptively decompose the vibration signal into multiple intrinsic mode functions (IMFs), and the representative IMFs which contained rich fault information were selected to reconstruct a feature vector space. Secondly, the multiscale permutation entropy (MPE) was used to calculate the complexity of reconstructed feature space. Finally, the value of multiscale permutation entropy was presented to a support vector machine for fault classification. The proposed diagnostic algorithm was applied to three groups of rolling bearing experiments. The experimental results indicate that the proposed method has better classification performance and robustness than other traditional methods." @default.
• W2946480175 created "2019-05-29" @default.
• W2946480175 creator A5002571994 @default.
• W2946480175 creator A5006387641 @default.
• W2946480175 date "2019-05-23" @default.
• W2946480175 modified "2023-09-26" @default.
• W2946480175 title "Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy" @default.
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• W2946480175 doi "https://doi.org/10.3390/e21050519" @default.
• W2946480175 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7515008" @default.
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• W2946480175 hasPublicationYear "2019" @default.
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