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Optimized multivariate multiscale slope entropy for nonlinear dynamic analysis of mechanical signals

Author

Listed:
  • Li, Yuxing
  • Tang, Bingzhao
  • Jiao, Shangbin
  • Zhou, Yuhan

Abstract

Slope entropy (SloEn) is an effective nonlinear dynamic method to represent the complexity of time series, which has been extensively applied to various mechanical signal processing. However, it is only applicable to the analysis of single-channel time series at a single scale. Additionally, thresholds γandδ of SloEn can affect the division of symbols. To address these limitations, this paper firstly develops multivariate SloEn (mvSloEn) and extends it to multiscale mvSloEn (mvMSloEn), which not only accounts for the correlation of time series complexity within and across channels, but also mirrors the complexity of multi-channel time series over multiple scales. Furthermore, the sea-horse optimization-mvMSloEn (SHO-mvMSloEn) is proposed through utilizing the sea-horse optimizer (SHO) to fine-tune the thresholds for improved performance. Finally, the proposed SHO-mvMSloEn is applied to three real-world datasets and the highest classification accuracies are all over 98 %, superior to the existing multivariate multiscale dispersion entropy (mvMDE), multivariate multiscale fuzzy entropy (mvMFE), and multivariate multiscale sample entropy (mvMSE), which demonstrates that the proposed SHO-mvMSloEn always exhibits the best feature extraction capability.

Suggested Citation

  • Li, Yuxing & Tang, Bingzhao & Jiao, Shangbin & Zhou, Yuhan, 2024. "Optimized multivariate multiscale slope entropy for nonlinear dynamic analysis of mechanical signals," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    • Handle: RePEc:eee:chsofr:v:179:y:2024:i:c:s0960077923013383
    DOI: 10.1016/j.chaos.2023.114436
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