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Entropy 2015, 17(9), 5965-5979;

Friction Signal Denoising Using Complete Ensemble EMD with Adaptive Noise and Mutual Information

School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Received: 20 July 2015 / Revised: 20 August 2015 / Accepted: 20 August 2015 / Published: 25 August 2015
(This article belongs to the Section Information Theory)
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During the measurement of friction force, the measured signal generally contains noise. To remove the noise and preserve the important features of the signal, a hybrid filtering method is introduced that uses the mutual information and a new waveform. This new waveform is the difference between the original signal and the sum of intrinsic mode functions (IMFs), which are obtained by empirical mode decomposition (EMD) or its improved versions. To evaluate the filter performance for the friction signal, ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), and complete ensemble EMD with adaptive noise (CEEMDAN) are employed in combination with the proposed filtering method. The combination is used to filter the synthesizing signals at first. For the filtering of the simulation signal, the filtering effect is compared under conditions of different ensemble number, sampling frequency, and the input signal-noise ratio, respectively. Results show that CEEMDAN outperforms other signal filtering methods. In particular, this method is successful in filtering the friction signal as evaluated by the de-trended fluctuation analysis (DFA) algorithm. View Full-Text
Keywords: CEEMDAN; filtering; mutual information; friction signal; DFA CEEMDAN; filtering; mutual information; friction signal; DFA

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, C.; Zhan, L.; Shen, L. Friction Signal Denoising Using Complete Ensemble EMD with Adaptive Noise and Mutual Information. Entropy 2015, 17, 5965-5979.

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