Refined Composite Multiscale Phase Rényi Dispersion Entropy for Complexity Measure

Yu-Han Tong, Guang Ling, Z. Guan, Qingju Fan, Li Wan
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引用次数: 0

Abstract

Assessing the complexity of signals or dynamical systems is important in disease diagnosis, mechanical system defect, astronomy analysis, and many other fields. Although entropy measures as complexity estimators have greatly improved, the majority of these measures are quite sensitive to specified parameters and are impacted by short data lengths. This paper proposes a novel entropy algorithm to enhance the existing complexity assessment methods based on classical dispersion entropy (DE) and Rényi entropy (RE) by introducing refined composite multiscale coarse-grained treatment and phase transformation. The proposed refined composite multiscale phase Rényi dispersion entropy (PRRCMDE) addresses the flaws of various existing entropy approaches while still incorporating their merits. Several simulated signals from logistic mapping, AR model, MIX process, and additive WGN periodic signals are adopted to examine the performance of PRRCMDE from multiple perspectives. It demonstrates that the efficacy of the suggested algorithm can be increased by modifying the DE and RE parameters to a reasonable range. As a real-world application, the bearings’ varied fault types and levels can also be recognized clearly.
复杂性度量的精细复合多尺度相r色散熵
评估信号或动力系统的复杂性在疾病诊断、机械系统缺陷、天文分析和许多其他领域都很重要。虽然熵测度作为复杂度估计器已经有了很大的改进,但是大多数熵测度对特定参数非常敏感,并且受到短数据长度的影响。针对现有的基于经典色散熵(DE)和rsamnyi熵(RE)的复杂性评估方法,本文提出了一种新的熵算法,通过引入精细复合多尺度粗粒度处理和相变,对现有的复杂性评估方法进行了改进。提出的改进复合多尺度相r尼米色散熵(PRRCMDE)方法在吸收现有熵方法优点的同时,解决了现有熵方法的缺陷。采用逻辑映射、AR模型、MIX过程和附加WGN周期信号等多个仿真信号,从多个角度考察了PRRCMDE的性能。结果表明,将DE和RE参数调整到合理的范围内,可以提高算法的有效性。作为实际应用,也可以清楚地识别轴承的各种故障类型和级别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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