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首页> 外文期刊>Mechanical systems and signal processing >Two-dimensional time series sample entropy algorithm: Applications to rotor axis orbit feature identification
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Two-dimensional time series sample entropy algorithm: Applications to rotor axis orbit feature identification

机译：二维时间序列样本熵算法：应用于转子轴轨道特征识别

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摘要

Traditional sample entropy algorithms are limited in their inability to analyze two-dimensional (2D) time series. Here, we describe a new feature algorithm for 2D time-series complexity and signal classification. This is a 2D sample entropy algorithm that includes the definitions of distance d and tolerance r in the 2D sample entropy algorithm on 2D signal scale and the difference between 2D and 1D sample entropies. The effectiveness of this algorithm in characterizing 2D signals was verified through simulated signal analysis. Then, by combining the 2D sample entropy algorithm with ensemble empirical mode decomposition (EEMD) and support vector machine (SVM) algorithms, we proposed a magnetically suspended rotor axis orbit feature identification and fault diagnosis method based on 2D sample entropy. This method was used to first perform EEMD of the 2D signals of a magnetically suspended rotor axis orbit to obtain several intrinsic mode functions (IMFs). We then calculated the 2D sample entropies of each IMF, and finally input the two-dimensional sample entropy as 2D feature vectors separately into the SVM, neural network, and logistic regression to identify the features of a rotor axis orbit. Finally, we compared the ID sample entropy and variational mode decomposition (VMD) sample entropy. A comparison of experimental results showed that the 2D sample entropy algorithm can be used to characterize 2D signals, identify the features of the rotor axis orbit based on typical 2D signals, and identify and classify the rotor axis orbits under different fault conditions. The performance of this algorithm in feature identification is remarkably superior to that of 1D and VMD sample entropy algorithms. The availability of online diagnosis of this method was verified via speed testing.
机译：传统的样本熵算法在无法分析二维（2D）时间序列中受到限制。这里，我们描述了一种用于2D时间序列复杂度和信号分类的新特征算法。这是一个2D样本熵算法，包括在2D信号刻度的2D样品熵算法中的距离D和公差R和2D和1D样本熵之间的差异。通过模拟信号分析验证了该算法在表征2D信号时的有效性。然后，通过将2D样本熵算法与集合经验模式分解（EEMD）组合并支持向量机（SVM）算法，我们提出了一种基于2D样品熵的磁悬浮转子轴轨迹特征识别和故障诊断方法。该方法首先执行磁悬浮转子轴轨道的2D信号的EEMD，以获得几种内在模式（IMF）。然后，我们计算了每个IMF的2D样本熵，最后将二维样本熵作为2D特征向量分别输入到SVM，神经网络和逻辑回归中，以识别转子轴轨道的特征。最后，我们比较了ID样本熵和变分模式分解（VMD）样本熵。实验结果的比较表明，2D样品熵算法可用于表征2D信号，基于典型的2D信号识别转子轴轨道的特征，并在不同的故障条件下识别和分类转子轴轨道。该算法在特征识别中的性能显着优于1D和VMD样本熵算法。通过速度测试验证了这种方法的在线诊断的可用性。

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