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Open AccessArticle

Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy

School of Hydropower & Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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Entropy 2019, 21(5), 519; https://doi.org/10.3390/e21050519
Received: 24 April 2019 / Revised: 17 May 2019 / Accepted: 21 May 2019 / Published: 23 May 2019
(This article belongs to the Section Signal and Data Analysis)
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. View Full-Text
Keywords: fault diagnosis; rolling element bearings; ensemble empirical mode decomposition; feature space reconstruction; multiscale permutation entropy fault diagnosis; rolling element bearings; ensemble empirical mode decomposition; feature space reconstruction; multiscale permutation entropy
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Zhang, W.; Zhou, J. Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy. Entropy 2019, 21, 519.

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