Time Series Analysis Using Composite Multiscale Entropy
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) o...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-03-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1099-4300/15/3/1069 |
| _version_ | 1798034053626068992 |
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| author | Kung-Yen Lee Chun-Chieh Wang Shiou-Gwo Lin Chiu-Wen Wu Shuen-De Wu |
| author_facet | Kung-Yen Lee Chun-Chieh Wang Shiou-Gwo Lin Chiu-Wen Wu Shuen-De Wu |
| author_sort | Kung-Yen Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:37:45Z |
| format | Article |
| id | doaj.art-5f7e059068624055a5aa4fd5b706eb83 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T20:37:45Z |
| publishDate | 2013-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-5f7e059068624055a5aa4fd5b706eb832022-12-22T04:04:18ZengMDPI AGEntropy1099-43002013-03-011531069108410.3390/e15031069Time Series Analysis Using Composite Multiscale EntropyKung-Yen LeeChun-Chieh WangShiou-Gwo LinChiu-Wen WuShuen-De WuMultiscale 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.http://www.mdpi.com/1099-4300/15/3/1069composite multiscale entropymultiscale entropyfault diagnosis |
| spellingShingle | Kung-Yen Lee Chun-Chieh Wang Shiou-Gwo Lin Chiu-Wen Wu Shuen-De Wu Time Series Analysis Using Composite Multiscale Entropy Entropy composite multiscale entropy multiscale entropy fault diagnosis |
| title | Time Series Analysis Using Composite Multiscale Entropy |
| title_full | Time Series Analysis Using Composite Multiscale Entropy |
| title_fullStr | Time Series Analysis Using Composite Multiscale Entropy |
| title_full_unstemmed | Time Series Analysis Using Composite Multiscale Entropy |
| title_short | Time Series Analysis Using Composite Multiscale Entropy |
| title_sort | time series analysis using composite multiscale entropy |
| topic | composite multiscale entropy multiscale entropy fault diagnosis |
| url | http://www.mdpi.com/1099-4300/15/3/1069 |
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