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Open AccessArticle

The Optimized Multi-Scale Permutation Entropy and Its Application in Compound Fault Diagnosis of Rotating Machinery

by Xianzhi Wang 1, Shubin Si 1, Yu Wei 2,* and Yongbo Li 3
1
School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
2
Department of Astronautical Science and Mechanics, Harbin Institute of Technology (HIT), Harbin 150001, China
3
School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(2), 170; https://doi.org/10.3390/e21020170
Received: 20 January 2019 / Revised: 2 February 2019 / Accepted: 3 February 2019 / Published: 12 February 2019
(This article belongs to the Special Issue Information-Theoretical Methods in Data Mining)
Multi-scale permutation entropy (MPE) is a statistic indicator to detect nonlinear dynamic changes in time series, which has merits of high calculation efficiency, good robust ability, and independence from prior knowledge, etc. However, the performance of MPE is dependent on the parameter selection of embedding dimension and time delay. To complete the automatic parameter selection of MPE, a novel parameter optimization strategy of MPE is proposed, namely optimized multi-scale permutation entropy (OMPE). In the OMPE method, an improved Cao method is proposed to adaptively select the embedding dimension. Meanwhile, the time delay is determined based on mutual information. To verify the effectiveness of OMPE method, a simulated signal and two experimental signals are used for validation. Results demonstrate that the proposed OMPE method has a better feature extraction ability comparing with existing MPE methods. View Full-Text
Keywords: rotating machinery; parameter selection; multi-scale permutation entropy; mutual information; improved Cao method rotating machinery; parameter selection; multi-scale permutation entropy; mutual information; improved Cao method
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Wang, X.; Si, S.; Wei, Y.; Li, Y. The Optimized Multi-Scale Permutation Entropy and Its Application in Compound Fault Diagnosis of Rotating Machinery. Entropy 2019, 21, 170.

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