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Multiscale time-frequency dispersion pattern entropy: A new nonlinear dynamics feature extraction method for rolling bearing fault diagnosis

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  • Li, Yuxing
  • Lou, Yilan
  • Zhang, Yu

Abstract

This study focuses on the limitation of existing entropy-based nonlinear dynamics methods, such as dispersion entropy and permutation entropy, in machinery fault diagnosis, which only consider the time-domain information of signals, neglecting the inherent nonlinear dynamics properties in the frequency domain. To address these limitations, a novel time-frequency dispersion pattern entropy (TFDPE), which integrates the frequency domain features into the dispersal pattern framework of the conventional dispersal entropy and extends it to the time-frequency dispersal pattern, thus improving the overall accuracy of the signal characterization. Experimental results indicate that TFDPE outperforms other entropy methods in characterizing the complexity and robustness of dynamic signals, achieving optimal classification performance in bearing fault diagnosis.

Suggested Citation

  • Li, Yuxing & Lou, Yilan & Zhang, Yu, 2025. "Multiscale time-frequency dispersion pattern entropy: A new nonlinear dynamics feature extraction method for rolling bearing fault diagnosis," Chaos, Solitons & Fractals, Elsevier, vol. 200(P3).
    • Handle: RePEc:eee:chsofr:v:200:y:2025:i:p3:s096007792501080x
    DOI: 10.1016/j.chaos.2025.117067
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    References listed on IDEAS

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    1. Wang, Zhuo & Shang, Pengjian, 2021. "Generalized entropy plane based on multiscale weighted multivariate dispersion entropy for financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    2. 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).
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