Reverse Dispersion Entropy: A New Complexity Measure for Sensor Signal
Reverse Dispersion Entropy: A New Complexity Measure for Sensor Signal
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DOI：
10.3390/s19235203
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发表时间：
2019-12-01
期刊：
影响因子：
3.9
通讯作者：
Wang, Long
中科院分区：
文献类型：
--
作者：
Li, Yuxing;Gao, Xiang;Wang, Long
排列熵作为时间序列分析的一种强有力的复杂性度量，具有易于实现和效率高等优点。为了提高PE的性能，近年来，通过引入幅度信息和距离信息，提出了一些改进的PE方法。加权排列熵（W-PE）利用方差信息对各排列模式进行加权，在高噪声水平下具有良好的鲁棒性和稳定性，能够从具有尖峰特征或幅度突变的数据中提取复杂度信息。色散熵利用正态累积分布函数（NCDF）引入幅度信息，不仅可以检测频率和幅度同时变化，而且在区分不同数据集方面优于PE方法，具有上级优势。反向排列熵（RPE）定义为与PE和W-PE趋势相反的白色噪声的距离，对变长度时间序列具有很高的稳定性。为了进一步提高PE的性能，我们提出了一种新的时间序列分析的复杂性度量，并称之为反向离散熵（RDE）。RDE以概率熵为理论基础，通过引入幅度信息和距离信息，结合了DE和RPE的优点。对仿真信号和传感器信号进行了仿真实验，包括不同参数下的突变信号检测、噪声鲁棒性测试、不同信噪比下的稳定性测试，以及区分不同类型船舶和故障的真实的数据。实验结果表明，与PE、W-PE、RPE和DE相比，RDE在检测突变信号和噪声鲁棒性测试中具有更好的性能，对模拟信号和传感器信号具有更好的稳定性。此外，它也表现出更高的区分能力比其他四种PE的传感器信号。
Permutation entropy (PE), as one of the powerful complexity measures for analyzing time series, has advantages of easy implementation and high efficiency. In order to improve the performance of PE, some improved PE methods have been proposed through introducing amplitude information and distance information in recent years. Weighted-permutation entropy (W-PE) weight each arrangement pattern by using variance information, which has good robustness and stability in the case of high noise level and can extract complexity information from data with spike feature or abrupt amplitude change. Dispersion entropy (DE) introduces amplitude information by using the normal cumulative distribution function (NCDF); it not only can detect the change of simultaneous frequency and amplitude, but also is superior to the PE method in distinguishing different data sets. Reverse permutation entropy (RPE) is defined as the distance to white noise in the opposite trend with PE and W-PE, which has high stability for time series with varying lengths. To further improve the performance of PE, we propose a new complexity measure for analyzing time series, and term it as reverse dispersion entropy (RDE). RDE takes PE as its theoretical basis and combines the advantages of DE and RPE by introducing amplitude information and distance information. Simulation experiments were carried out on simulated and sensor signals, including mutation signal detection under different parameters, noise robustness testing, stability testing under different signal-to-noise ratios (SNRs), and distinguishing real data for different kinds of ships and faults. The experimental results show, compared with PE, W-PE, RPE, and DE, that RDE has better performance in detecting abrupt signal and noise robustness testing, and has better stability for simulated and sensor signal. Moreover, it also shows higher distinguishing ability than the other four kinds of PE for sensor signals.