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A Method for Detecting Dynamic Mutation of Complex Systems Using Improved Information Entropy

1
College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China
2
National Engineering Laboratory of Energy-Saving Motor & Control Technology, Anhui University, Hefei 230601, China
3
College of Mathematical Sciences, Anhui University, Hefei 230601, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(2), 115; https://doi.org/10.3390/e21020115
Received: 18 December 2018 / Revised: 12 January 2019 / Accepted: 22 January 2019 / Published: 27 January 2019
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
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Abstract

In this study, a nonlinear analysis method called improved information entropy (IIE) is proposed on the basis of constructing a special probability mass function for the normalized analysis of Shannon entropy for a time series. The definition is directly applied to several typical time series, and the characteristic of IIE is analyzed. This method can distinguish different kinds of signals and reflects the complexity of one-dimensional time series of high sensitivity to the changes in signal. Thus, the method is applied to the fault diagnosis of a rolling bearing. Experimental results show that the method can effectively extract the sensitive characteristics of the bearing running state and has fast operation time and minimal parameter requirements. View Full-Text
Keywords: nonlinear dynamics; probability mass function; improved information entropy; rolling bearing; fault diagnosis nonlinear dynamics; probability mass function; improved information entropy; rolling bearing; fault diagnosis
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Ju, B.; Zhang, H.; Liu, Y.; Pan, D.; Zheng, P.; Xu, L.; Li, G. A Method for Detecting Dynamic Mutation of Complex Systems Using Improved Information Entropy. Entropy 2019, 21, 115.

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