Time Series Analysis Using Composite Multiscale Entropy

Entropy

Shuen-De Wu²  Chiu-Wen Wu²  Shiou-Gwo Lin¹  Chun-Chieh Wang³ 

[1] Department of Communication, Navigation and Control Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan; E-Mail:;Department of Mechatronic Technology, National Taiwan Normal University, Taipei 10610, Taiwan; E-Mails:;Mechanical and Systems Research Laboratories, Industrial Technology Research Institute, Hsinchu 31040, Taiwan; E-Mail:

关键词: composite multiscale entropy;    multiscale entropy;    fault diagnosis;   

DOI  :  10.3390/e15031069

来源: mdpi

【 摘 要 】

Multiscale entropy (MSE) was recently developed to evaluate the complexity of time series over different time scales. Although the MSE algorithm has been successfully applied in a number of different fields, it encounters a problem in that the statistical reliability of the sample entropy (SampEn) of a coarse-grained series is reduced as a time scale factor is increased. Therefore, in this paper, the concept of a composite multiscale entropy (CMSE) is introduced to overcome this difficulty. Simulation results on both white noise and 1/f noise show that the CMSE provides higher entropy reliablity than the MSE approach for large time scale factors. On real data analysis, both the MSE and CMSE are applied to extract features from fault bearing vibration signals. Experimental results demonstrate that the proposed CMSE-based feature extractor provides higher separability than the MSE-based feature extractor.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.