Generalized sample entropy analysis for traffic signals based on similarity measure

Shang, Du; Xu, Mengjia; Shang, Pengjian

doi:10.1016/j.physa.2017.01.061

Cited by 11 publications

(3 citation statements)

References 31 publications

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“…In literature, there are numerous approaches examining the complexity and dynamics of traffic flow and one of them is the use of entropy-based methods. In terms of this, recent various studies, e.g., [1][2][3][4][5][6] would be typical in the vehicular traffic arena. Of these, for example, Kosun and Ozdemir [1] focus on the platoon formation of vehicles and propose an upper and a lower limit of Tsallis q entropic index.…”

Section: Introductionmentioning

confidence: 98%

Examining vehicular speed characteristics through divergences from prior distributions

Koşun

2021

SN Appl. Sci.

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A variety of approaches, within literature, has been conducted to interpret vehicular speed characteristics. This study turns the attention to the entropy-based approaches, and thus focuses on the maximum entropy method of statistical mechanics and the Kullback–Leibler (KL) divergence approach to examining the vehicular speeds. The vehicle speeds at the selected highway are analyzed in order to find out the disparities among them. However, it is turned out that the speed dynamics could not be distinguished over the speed distributions; hence the maximization of Shannon entropy seems insufficient to compare the speed distributions of each data set. For this reason, the KL divergence approach was performed. This approach displays the comparison, among the speed distributions, based on two prior distribution models, i.e., uniform and Gauss. The examination of the trends of KL divergences obtained from both distributions was made. It was concluded that the KL divergence values for the highway speed data sets ranged between about 0.53 and 0.70 for the uniform case, while for the Gaussian case the obtained values are between 0.16 and 0.33. The KL divergence trends for the real speeds were obtained analogous for both cases, but they differed significantly when the synthetic data sets were employed. As a result, the KL divergence approach proves suitable as an appropriate indicator to compare the speed distributions.

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Section: Introductionmentioning

confidence: 98%

Examining vehicular speed characteristics through divergences from prior distributions

Koşun

2021

SN Appl. Sci.

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“…The correlation coefficient [27,28] and Kullback-Leibler (K-L) divergence [29][30][31] do not work well for identifying faults. Recently, sample entropy (SE) [32,33], approximate entropy (AE) [34,35] and fuzzy entropy (FE) [36,37] were introduced into the fault diagnosis domain. However, at the boundary, SE is changed suddenly and not continuously.…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Fault Feature Extraction of Rolling Bearings Based on Enhancing Mode Characteristic of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Zhan

et al. 2019

Symmetry

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Originally, a rolling bearing, as a key part in rotating machinery, is a cyclic symmetric structure. When a fault occurs, it disrupts the symmetry and influences the normal operation of the rolling bearing. To accurately identify faults of rolling bearing, a novel method is proposed, which is based enhancing the mode characteristics of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). It includes two parts: the first is the enhancing decomposition of CEEMDAN algorithm, and the second is the identified method of intrinsic information mode (IIM) of vibration signal. For the first part, the new mode functions (CIMFs) are obtained by combing the adjacent intrinsic mode functions (IMFs) and performing the corresponding Fast Fourier Transform (FFT) to strengthen difference feature among IMFs. Then, probability density function (PDF) is used to estimate FFT of each CIMF to obtain overall information of frequency component. Finally, the final intrinsic mode functions (FIMFs) are obtained by proposing identified method of adjacent PDF based on geometrical similarity (modified Hausdorff distance (MHD)). FIMFs indicate the minimum amount of mode information with physical meanings and avoid interference of spurious mode in original CEEMDAN decomposing. Subsequently, comprehensive evaluate index (Kurtosis and de-trended fluctuation analysis (DFA)) is proposed to identify IIM in FIMFs. Experiment results indicate that the proposed method demonstrates superior performance and can accurately extract characteristic frequencies of rolling bearing.

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“…In recent years, the complexity of time series has served as an essential property for deeply understanding the non-stationary characteristics of vibration signals from mechanical systems [1,2]. Entropy-based complexity measurement methods such as approximate entropy (ApEn) [3][4][5], sample entropy (SampEn) [6][7][8], fuzzy entropy (FuzzyEn) [9,10] and permutation entropy (PE) [11,12] have been significantly important technologies to evaluate the dynamical complexity of time series. Generally, higher entropy indicates higher uncertainty and lower entropy indicates more regularity and certainty of a system [13,14].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Distribution Entropy and t-Distributed Stochastic Neighbor Embedding-Based Fault Diagnosis of Rolling Bearings

Zheng

Jiang

et al. 2018

Entropy

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As a nonlinear dynamic method for complexity measurement of time series, multiscale entropy (MSE) has been successfully applied to fault diagnosis of rolling bearings. However, the MSE algorithm is sensitive to the predetermined parameters and depends heavily on the length of the time series and MSE may yield an inaccurate estimation of entropy or undefined entropy when the length of time series is too short. To improve the robustness of complexity measurement for short time series, a novel nonlinear parameter named multiscale distribution entropy (MDE) was proposed and employed to extract the nonlinear complexity features from vibration signals of rolling bearing in this paper. Combining with t-distributed stochastic neighbor embedding (t-SNE) for feature dimension reduction and Kriging-variable predictive models based class discrimination (KVPMCD) for automatic identification, a new intelligent fault diagnosis method for rolling bearings was proposed. Finally, the proposed approach was applied to analyze the experimental data of rolling bearings and the results indicated that the proposed method could distinguish the different fault categories of rolling bearings effectively.

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Generalized sample entropy analysis for traffic signals based on similarity measure

Cited by 11 publications

References 31 publications

Examining vehicular speed characteristics through divergences from prior distributions

Examining vehicular speed characteristics through divergences from prior distributions

Self-Adaptive Fault Feature Extraction of Rolling Bearings Based on Enhancing Mode Characteristic of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Multiscale Distribution Entropy and t-Distributed Stochastic Neighbor Embedding-Based Fault Diagnosis of Rolling Bearings

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