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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40671

Title: Feature Extraction for Bearing Fault Diagnosis Using Composite Multiscale Entropy
Authors: Shuen-De Wu;Chiu-Wen Wu;Shiou-Gwo Lin;Chun-Chieh Wang;Kung-Yen Lee
Contributors: 國立臺灣海洋大學:通訊與導航工程學系
Date: 2013
Issue Date: 2017-02-06T03:28:34Z
Publisher: Advanced Intelligent Mechatronics (AIM), 2013 IEEE/ASME International Conference on
Abstract: Abstract: Multiscale entropy (MSE) is a popular algorithm to measure the complexity of a time series for multiple scales. However, the conventional MSE algorithm yields imprecise estimation of entropy for a time series with large time scale factors. In this paper, a composite multiscale entropy (CMSE) method is proposed to overcome this drawback. In the CMSE algorithm, with scale factors of τ, we calculate the sample entropies (SampEns) of all coarse-grained series and then define the mean of τ SampEns as the entropy values. This proposed algorithm is then applied to two different kinds of simulated noise signals and a set of real vibration data. These results demonstrate that the proposed CMSE provides more precise entropy calculation than the convectional MSE. Furthermore, as a feature extractor for a bearing faulty signal, CMSE provides a higher distinguishability, compared with MSE.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40671
Appears in Collections:[通訊與導航工程學系] 演講及研討會

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