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Edge permutation entropy: an improved entropy measure for time-series analysis

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conference contribution
posted on 03.08.2021, 15:52 authored by Zhiqiang Huo, Eve ZhangEve Zhang, Lei Shu, Xiaowen Liao
Permutation Entropy (PE) has been widely applied as a non-linear statistical indicator to estimate the change of complexity in time series. Though it is conceptually simple and computationally fast, PE encounters a few limitations. For example, the amplitude differences in time series are neglected, and the symbolic sequences generated from equal values are according to their emergence order. In this paper, an Edge Permutation Entropy (EdgePE) measure is proposed to improve the performance of PE, mainly overcoming its lack of ability to differentiate between amplitude differences in motifs that correspond to the same order pattern. The advantage of EdgePE relies on that amplitude change information can be identified and distinguished by the information underlying in the 'edge' distance between data points in the reconstructed embedded vectors. To demonstrate its improvement, the proposed EdgePE is compared with other related improved PE approaches, for analyzing synthetic time series and experimental rolling bearing data sets, respectively. The results indicate that the EdgePE can effectively characterize amplitude changes in time series (e.g., for spike and stuck detection) and improve the accuracy of pattern recognition for rolling bearing fault diagnosis, compared to those of the other related PE measures.


International and Hong Kong, Macao & Taiwan collaborative innovation platform and major international cooperation projects of colleges in Guangdong Province (No.2015KGJHZ026)

The Natural Science Foundation of Guangdong Province (No.2016A030307029)



  • Aeronautical, Automotive, Chemical and Materials Engineering


  • Aeronautical and Automotive Engineering

Published in

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society


5998 - 6003




AM (Accepted Manuscript)

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© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date


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Lisbon, Portugal

Event dates

14th October 2019 - 17th October 2019


Dr Eve Zhang. Deposit date: 29 July 2021