Time series analysis using composite multiscale entropy

Shuen De Wu, Chiu Wen Wu, Shiou Gwo Lin, Chun Chieh Wang, Kung Yen Lee

Research output: Contribution to journalArticle

152 Citations (Scopus)

Abstract

Multiscale entropy (MSE) was recently developed to evaluate the complexity of time series over different time scales. Although the MSE algorithm has been successfully applied in a number of different fields, it encounters a problem in that the statistical reliability of the sample entropy (SampEn) of a coarse-grained series is reduced as a time scale factor is increased. Therefore, in this paper, the concept of a composite multiscale entropy (CMSE) is introduced to overcome this difficulty. Simulation results on both white noise and 1/f noise show that the CMSE provides higher entropy reliablity than the MSE approach for large time scale factors. On real data analysis, both the MSE and CMSE are applied to extract features from fault bearing vibration signals. Experimental results demonstrate that the proposed CMSE-based feature extractor provides higher separability than the MSE-based feature extractor.

Original languageEnglish
Pages (from-to)1069-1084
Number of pages16
JournalEntropy
Volume15
Issue number3
DOIs
Publication statusPublished - 2013 Mar

Keywords

  • Composite multiscale entropy
  • Fault diagnosis
  • Multiscale entropy

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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  • Cite this

    Wu, S. D., Wu, C. W., Lin, S. G., Wang, C. C., & Lee, K. Y. (2013). Time series analysis using composite multiscale entropy. Entropy, 15(3), 1069-1084. https://doi.org/10.3390/e15031069