Fractional Order Fuzzy Dispersion Entropy and Its Application in Bearing Fault Diagnosis
Fractional Order Fuzzy Dispersion Entropy and Its Application in Bearing Fault Diagnosis
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分数阶模糊离散熵及其在轴承故障诊断中的应用
DOI：
10.3390/fractalfract6100544
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发表时间：
2022-10-01
影响因子：
5.4
通讯作者：
Jiao, Shangbin
中科院分区：
文献类型：
--
作者：
Li, Yuxing;Tang, Bingzhao;Jiao, Shangbin
模糊离散熵（FuzzDE）是最近提出的一种非线性动态指标，它结合了离散熵（DE）和模糊熵（FuzzEn）的优点来检测时间序列的动态变化。然而，FuzzDE只反映了原始信号的信息，对动态变化不太敏感。针对这些缺点，在FuzzDE的基础上引入分数阶计算，提出了FuzzDE（alpha），并将其作为特征用于轴承信号分析和故障诊断。此外，还介绍了分数阶DE（alpha）、分数阶置换熵（PE alpha）和基于分数阶波动的DE（FDE alpha）等分数阶熵，并提出了一种混合特征提取的诊断方法。仿真和真实实验结果均表明，不同分数阶的FuzzDE（alpha）对时间序列的动态变化更敏感，所提出的混合特征故障诊断方法仅在三重特征下就达到了100%的识别率，其中识别率最高的混合特征组合都具有FuzzDE（alpha），而FuzzDE（alpha）也是出现最频繁的。
Fuzzy dispersion entropy (FuzzDE) is a very recently proposed non-linear dynamical indicator, which combines the advantages of both dispersion entropy (DE) and fuzzy entropy (FuzzEn) to detect dynamic changes in a time series. However, FuzzDE only reflects the information of the original signal and is not very sensitive to dynamic changes. To address these drawbacks, we introduce fractional order calculation on the basis of FuzzDE, propose FuzzDE(alpha) and use it as a feature for the signal analysis and fault diagnosis of bearings. In addition, we also introduce other fractional order entropies, including fractional order DE (DE)(alpha), fractional order permutation entropy (PE alpha) and fractional order fluctuation-based DE (FDE alpha), and propose a mixed features extraction diagnosis method. Both simulated as well as real-world experimental results demonstrate that the FuzzDE(alpha) at different fractional orders is more sensitive to changes in the dynamics of the time series, and the proposed mixed features bearing fault diagnosis method achieves 100% recognition rate at just triple features, among which, the mixed feature combinations with the highest recognition rates all have FuzzDE(alpha), and FuzzDE(alpha) also appears most frequently.