Li Yuxing, Wu Junxian, Zhang Shuai, Tang Bingzhao, Lou Yilan
School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China.
Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China.
Entropy (Basel). 2023 Jun 29;25(7):997. doi: 10.3390/e25070997.
Fuzzy dispersion entropy (FuzDE) is a newly proposed entropy metric, which combines the superior characteristics of fuzzy entropy (FE) and dispersion entropy (DE) in signal analysis. However, FuzDE only reflects the feature from the original signal, which ignores the hidden information on the time scale. To address this problem, we introduce variable-step multiscale processing in FuzDE and propose variable-step multiscale FuzDE (VSMFuzDE), which realizes the characterization of abundant scale information, and is not limited by the signal length like the traditional multiscale processing. The experimental results for both simulated signals show that VSMFuzDE is more robust, more sensitive to dynamic changes in the chirp signal, and has more separability for noise signals; in addition, the proposed VSMFuzDE displays the best classification performance in both real-world signal experiments compared to the other four entropy metrics, the highest recognition rates of the five gear signals and four ship-radiated noises reached 99.2% and 100%, respectively, which achieves the accurate identification of two different categories of signals.
模糊分散熵（FuzDE）是一种新提出的熵度量，它结合了模糊熵（FE）和分散熵（DE）在信号分析中的优越特性。然而，FuzDE仅反映原始信号的特征，忽略了时间尺度上的隐藏信息。为了解决这个问题，我们在FuzDE中引入可变步长多尺度处理，并提出可变步长多尺度FuzDE（VSMFuzDE），它实现了丰富尺度信息的表征，并且不像传统多尺度处理那样受信号长度的限制。对模拟信号的实验结果表明，VSMFuzDE更稳健，对啁啾信号的动态变化更敏感，对噪声信号具有更强的可分离性；此外，与其他四种熵度量相比，所提出的VSMFuzDE在实际信号实验中均表现出最佳的分类性能，五种齿轮信号和四种船舶辐射噪声的最高识别率分别达到99.2%和100%，实现了对两类不同信号的准确识别。
